Self-play loop, Qwen3.5-9B bake-off: 70% base accuracy
Self-play (training/scripts/self_play.py): - Model generates edge-case prompts across 9 categories - Attempts commands via RCON, self-corrects on errors - Successful traces → standard training examples - Error correction traces → multi-turn tool-calling examples - Anti-collapse: focuses on categories model is weakest in - Ready for v4 deployment, not yet active Qwen3.5-9B base model bake-off (147/1542 cases): - 70.1% OK (vs 34% Qwen3-8B base) — 2x improvement - 29.9% MISS (mostly God/prayer — no persona training) - 15.6% needed syntax fixes - Avg 7.5s response (thinking tokens) - Strong v4 candidate: better base + tool-calling architecture Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""
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self_play.py — Self-play training data generator.
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The fine-tuned model generates its own training data by:
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1. Generating diverse edge-case prompts it's uncertain about
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2. Attempting commands via RCON
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3. Self-correcting on errors
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4. Saving successful sequences as training examples
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This creates a closed-loop learning system with RCON as ground truth.
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No API cost — runs entirely on the local model.
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Usage:
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python3 training/scripts/self_play.py --rounds 100 --model qwen3.5-9b-mc-v4
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python3 training/scripts/self_play.py --rounds 50 --dry-run
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python3 training/scripts/self_play.py --rounds 200 --focus enchantments
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"""
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import argparse
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import json
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import os
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import re
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import random
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import sys
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import time
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from pathlib import Path
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import requests
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ROOT = Path(__file__).resolve().parent.parent.parent
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sys.path.insert(0, str(ROOT))
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OUTPUT = ROOT / "data" / "processed" / "self_play.jsonl"
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# --- RCON ---
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def rcon_command(cmd, host, port, password):
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"""Execute via RCON, return (success, result_text)."""
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import socket, struct
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try:
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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s.settimeout(5)
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s.connect((host, port))
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def send(rid, ptype, payload):
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data = struct.pack("<ii", rid, ptype) + payload.encode("utf-8") + b"\x00\x00"
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s.sendall(struct.pack("<i", len(data)) + data)
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def recv():
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raw = s.recv(4)
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if len(raw) < 4: return None
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length = struct.unpack("<i", raw)[0]
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data = s.recv(length)
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return data[8:-2].decode("utf-8", errors="replace")
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send(1, 3, password)
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time.sleep(0.1)
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recv()
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send(2, 2, cmd)
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time.sleep(0.2)
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result = recv() or ""
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s.close()
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# Detect errors
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error_patterns = [
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"Unknown or incomplete command",
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"Incorrect argument",
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"Expected whitespace",
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"Unknown item",
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"Invalid or unknown",
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"Expected",
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]
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is_error = any(p.lower() in result.lower() for p in error_patterns)
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return (not is_error, result.strip())
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except Exception as e:
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return (False, f"RCON error: {e}")
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# --- LLM calls ---
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def llm_call(model, system, user, ollama_url, temperature=0.7, max_tokens=500, fmt=None):
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"""Call Ollama and return content with think blocks stripped."""
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payload = {
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"model": model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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],
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"stream": False,
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"options": {"temperature": temperature, "num_predict": max_tokens},
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}
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if fmt:
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payload["format"] = fmt
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r = requests.post(f"{ollama_url}/api/chat", json=payload, timeout=120)
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r.raise_for_status()
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content = r.json()["message"]["content"]
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# Strip think blocks
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content = re.sub(r'<think>[\s\S]*?</think>\s*', '', content)
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return content.strip()
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# --- Prompt generation categories ---
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EXPLORATION_CATEGORIES = {
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"enchantment_combos": {
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"instruction": """Generate 5 Minecraft chat messages that test unusual or edge-case enchantment combinations.
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Include: mutually exclusive enchants, max level exceeded, enchants on wrong items, multi-enchant syntax.
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Every message must start with "sudo " or "pray ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"entity_nbt": {
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"instruction": """Generate 5 Minecraft chat messages that test entity spawning with unusual NBT data.
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Include: custom names, baby variants, colored sheep, armored mobs, riding/passengers, powered creepers.
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Every message must start with "sudo " or "pray ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"execute_chains": {
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"instruction": """Generate 5 Minecraft chat messages that require complex execute command chains.
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Include: nested execute, conditional execution, store results, dimension switching, targeting by gamemode/team.
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Every message must start with "sudo ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"edge_items": {
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"instruction": """Generate 5 Minecraft chat messages requesting obscure or easily-confused items.
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Include: items with color variants, items that changed names between versions, items with underscores,
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items people misspell (like "wooden_sword" vs "wood_sword", "cooked_beef" vs "steak").
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Every message must start with "sudo ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"worldedit": {
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"instruction": """Generate 5 Minecraft chat messages requesting WorldEdit operations.
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Include: shapes, selections, replacements, brushes, stacking, clipboard operations.
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Every message must start with "sudo ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"multiplayer": {
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"instruction": """Generate 5 Minecraft chat messages involving multiple players or complex selectors.
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Include: @a with exclusions, team commands, scoreboard operations, targeting by distance/gamemode.
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Use player names like: slingshooter08, SwiftWolf, DarkWolf, BraveWolf.
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Every message must start with "sudo ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"boundary_testing": {
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"instruction": """Generate 5 Minecraft chat messages that test safety boundaries.
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Include: requests for forbidden items, mass destruction, OP commands, but also requests that SEEM dangerous
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but are actually fine (like giving TNT to yourself, or killing your own mobs).
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Every message must start with "sudo " or "pray ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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"natural_language": {
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"instruction": """Generate 5 Minecraft chat messages phrased in unusual or creative natural language.
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Include: typos, slang, roleplay, indirect requests, questions, sarcasm, mixed languages.
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The AI should still be able to figure out what the player wants.
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Every message must start with "sudo " or "pray ".
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Return a JSON array of strings.""",
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"temperature": 1.2,
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},
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"cosmetic_effects": {
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"instruction": """Generate 5 Minecraft chat messages requesting cosmetic or dramatic effects.
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Include: particles, sounds, titles, tellraw formatting, fireworks, combination effects.
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Every message must start with "sudo " or "pray ".
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Return a JSON array of strings.""",
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"temperature": 1.0,
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},
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}
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# System prompts
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SUDO_SYSTEM = """You are a Minecraft 1.21 command translator. Return JSON: {"commands": ["cmd1", ...], "reasoning": "why"}
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Commands use minecraft: prefix. Enchantments: item[enchantments={name:level}]. Effects: effect give <player> minecraft:<effect> <seconds> <amplifier>.
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Do NOT start commands with /. Player name: slingshooter08."""
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GOD_SYSTEM = """You are God in a Minecraft server. Return JSON: {"message": "dramatic response", "commands": ["cmd1", ...], "reasoning": "why"}
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Commands use minecraft: prefix. Be dramatic but use valid 1.21 syntax. Player: slingshooter08."""
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RETRY_SYSTEM = """You are a Minecraft 1.21 command translator. Your previous command failed with an error.
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Analyze the error and return a corrected command.
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Return JSON: {"commands": ["corrected_cmd"], "reasoning": "what was wrong and how you fixed it"}"""
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def generate_prompts(model, ollama_url, category=None):
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"""Use the model to generate edge-case prompts for itself."""
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if category:
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cats = {category: EXPLORATION_CATEGORIES[category]}
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else:
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# Pick 2-3 random categories per round
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keys = random.sample(list(EXPLORATION_CATEGORIES.keys()), min(3, len(EXPLORATION_CATEGORIES)))
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cats = {k: EXPLORATION_CATEGORIES[k] for k in keys}
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prompts = []
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for cat_name, cat_config in cats.items():
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try:
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raw = llm_call(
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model=model,
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system="You are a Minecraft test case generator. Generate diverse edge cases for an AI training pipeline.",
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user=cat_config["instruction"],
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ollama_url=ollama_url,
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temperature=cat_config["temperature"],
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max_tokens=400,
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)
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# Parse JSON array
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cleaned = raw.replace("```json", "").replace("```", "").strip()
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match = re.search(r'\[[\s\S]*\]', cleaned)
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if match:
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items = json.loads(match.group())
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for item in items:
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if isinstance(item, str) and item.strip():
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prompts.append({"prompt": item.strip(), "category": cat_name})
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except Exception as e:
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print(f" [!] Prompt generation failed for {cat_name}: {e}")
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return prompts
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def attempt_command(model, ollama_url, prompt, rcon_host, rcon_port, rcon_pass, max_retries=2):
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"""
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Model generates a command for the prompt, executes via RCON.
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On error, model self-corrects up to max_retries times.
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Returns the full interaction trace.
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"""
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mode = "god" if prompt.lower().startswith("pray ") else "sudo"
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system = GOD_SYSTEM if mode == "god" else SUDO_SYSTEM
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trace = {
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"prompt": prompt,
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"mode": mode,
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"attempts": [],
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"final_success": False,
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"self_corrected": False,
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}
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# First attempt
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try:
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raw = llm_call(model, system, prompt, ollama_url, temperature=0.3, max_tokens=300, fmt="json")
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result = json.loads(raw)
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except (json.JSONDecodeError, Exception) as e:
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# Try extracting JSON
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match = re.search(r'\{[\s\S]*\}', raw if 'raw' in dir() else '')
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if match:
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try:
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result = json.loads(match.group())
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except:
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trace["attempts"].append({"commands": [], "error": f"JSON parse failed: {e}"})
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return trace
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else:
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trace["attempts"].append({"commands": [], "error": f"LLM failed: {e}"})
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return trace
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commands = result.get("commands") or []
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message = result.get("message") or ""
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reasoning = result.get("reasoning") or ""
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if not commands:
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trace["attempts"].append({
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"commands": [], "reasoning": reasoning, "message": message,
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"rcon_results": [], "all_success": True,
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})
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trace["final_success"] = True # Refusal/info is valid
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return trace
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# Execute commands via RCON
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rcon_results = []
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all_success = True
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for cmd in commands:
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success, rcon_result = rcon_command(cmd, rcon_host, rcon_port, rcon_pass)
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rcon_results.append({"command": cmd, "success": success, "result": rcon_result})
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if not success:
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all_success = False
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trace["attempts"].append({
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"commands": commands, "reasoning": reasoning, "message": message,
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"rcon_results": rcon_results, "all_success": all_success,
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})
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if all_success:
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trace["final_success"] = True
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return trace
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# Self-correction loop
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for retry in range(max_retries):
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# Build error context for the model
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failed_cmds = [r for r in rcon_results if not r["success"]]
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error_context = "\n".join(
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f"Command: {r['command']}\nError: {r['result']}" for r in failed_cmds
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)
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retry_prompt = f"Original request: {prompt}\n\nFailed commands:\n{error_context}\n\nPlease fix the commands."
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try:
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raw = llm_call(model, RETRY_SYSTEM, retry_prompt, ollama_url, temperature=0.2, max_tokens=300, fmt="json")
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result = json.loads(raw)
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except:
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match = re.search(r'\{[\s\S]*\}', raw if 'raw' in dir() else '')
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if match:
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try:
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result = json.loads(match.group())
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except:
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break
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else:
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break
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commands = result.get("commands") or []
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reasoning = result.get("reasoning") or ""
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if not commands:
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break
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# Execute corrected commands
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rcon_results = []
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all_success = True
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for cmd in commands:
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success, rcon_result = rcon_command(cmd, rcon_host, rcon_port, rcon_pass)
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rcon_results.append({"command": cmd, "success": success, "result": rcon_result})
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if not success:
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all_success = False
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trace["attempts"].append({
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"commands": commands, "reasoning": reasoning,
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"rcon_results": rcon_results, "all_success": all_success,
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"retry": retry + 1,
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})
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if all_success:
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trace["final_success"] = True
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trace["self_corrected"] = True
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break
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return trace
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def trace_to_training(trace):
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"""Convert a self-play trace to training examples."""
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examples = []
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prompt = trace["prompt"]
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mode = trace["mode"]
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if not trace["attempts"]:
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return examples
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# Single successful attempt → standard training pair
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if trace["final_success"] and len(trace["attempts"]) == 1:
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att = trace["attempts"][0]
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ex = {
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"id": f"selfplay-{int(time.time())}-{random.randint(0,999):03d}",
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"source": "self_play",
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"category": "command_gen",
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"input": {
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"user_message": prompt,
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"server_context": {"server_type": "paper", "version": "1.21.x"},
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},
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"output": {
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"reasoning": att.get("reasoning", ""),
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"commands": att.get("commands", []),
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"message": att.get("message", "") if mode == "god" else "",
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"safety_flags": [],
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},
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"metadata": {
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"difficulty": "medium",
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"validated": True,
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"risk_level": 3,
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"rcon_verified": True,
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"self_play": True,
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},
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}
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examples.append(ex)
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# Self-corrected → multi-turn tool-calling training example
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elif trace["self_corrected"] and len(trace["attempts"]) >= 2:
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messages = []
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# System
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system = GOD_SYSTEM if mode == "god" else SUDO_SYSTEM
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messages.append({"role": "system", "content": system})
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# User
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messages.append({"role": "user", "content": prompt})
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# First attempt (failed)
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first = trace["attempts"][0]
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for r in first.get("rcon_results", []):
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messages.append({
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"role": "assistant",
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"content": f'<tool_call>\n{{"name": "rcon.execute", "arguments": {{"command": "{r["command"]}"}}}}\n</tool_call>'
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})
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messages.append({
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"role": "tool",
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"content": json.dumps({"success": r["success"], "result": r["result"]})
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})
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# Successful retry
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last = trace["attempts"][-1]
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for r in last.get("rcon_results", []):
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messages.append({
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"role": "assistant",
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"content": f'<tool_call>\n{{"name": "rcon.execute", "arguments": {{"command": "{r["command"]}"}}}}\n</tool_call>'
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})
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messages.append({
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"role": "tool",
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"content": json.dumps({"success": r["success"], "result": r["result"]})
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})
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# Final response
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final_cmds = last.get("commands", [])
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final_response = {
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"commands": final_cmds,
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"reasoning": f"Self-corrected: {first.get('reasoning', '')} → {last.get('reasoning', '')}",
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}
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if mode == "god":
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final_response["message"] = first.get("message", "")
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messages.append({"role": "assistant", "content": json.dumps(final_response)})
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ex = {
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"id": f"selfplay-correction-{int(time.time())}-{random.randint(0,999):03d}",
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"source": "self_play",
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"type": "error_correction",
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"messages": messages,
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"metadata": {
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"self_play": True,
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"rcon_verified": True,
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"attempts": len(trace["attempts"]),
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},
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}
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examples.append(ex)
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return examples
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def main():
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parser = argparse.ArgumentParser(description="Self-play training data generator")
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parser.add_argument("--model", default="qwen3-8b-mc-lora-v3")
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parser.add_argument("--ollama-url", default="http://192.168.0.141:11434")
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parser.add_argument("--rcon-host", default="192.168.0.244")
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parser.add_argument("--rcon-port", type=int, default=25578)
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parser.add_argument("--rcon-pass", default="REDACTED_RCON")
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parser.add_argument("--rounds", type=int, default=50)
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parser.add_argument("--focus", default=None, choices=list(EXPLORATION_CATEGORIES.keys()))
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parser.add_argument("--output", default=str(OUTPUT))
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parser.add_argument("--dry-run", action="store_true")
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parser.add_argument("--max-retries", type=int, default=2)
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args = parser.parse_args()
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||||
|
||||
print(f"Self-play training data generator")
|
||||
print(f" Model: {args.model}")
|
||||
print(f" RCON: {args.rcon_host}:{args.rcon_port}")
|
||||
print(f" Rounds: {args.rounds}")
|
||||
print(f" Focus: {args.focus or 'all categories'}")
|
||||
print(f" Max retries: {args.max_retries}")
|
||||
print(f" Output: {args.output}")
|
||||
|
||||
all_examples = []
|
||||
stats = {
|
||||
"rounds": 0, "prompts_generated": 0, "attempts": 0,
|
||||
"success_first_try": 0, "self_corrected": 0, "failed": 0,
|
||||
"training_examples": 0, "by_category": {},
|
||||
}
|
||||
|
||||
for round_num in range(args.rounds):
|
||||
print(f"\n--- Round {round_num + 1}/{args.rounds} ---")
|
||||
|
||||
# Generate prompts
|
||||
prompts = generate_prompts(args.model, args.ollama_url, args.focus)
|
||||
if not prompts:
|
||||
print(" No prompts generated, skipping round")
|
||||
continue
|
||||
|
||||
stats["prompts_generated"] += len(prompts)
|
||||
print(f" Generated {len(prompts)} prompts")
|
||||
|
||||
for p in prompts:
|
||||
prompt = p["prompt"]
|
||||
cat = p["category"]
|
||||
stats["by_category"].setdefault(cat, {"total": 0, "success": 0, "corrected": 0})
|
||||
stats["by_category"][cat]["total"] += 1
|
||||
stats["attempts"] += 1
|
||||
|
||||
print(f" [{cat}] {prompt[:60]:60}", end="")
|
||||
|
||||
if args.dry_run:
|
||||
print(" [DRY RUN]")
|
||||
continue
|
||||
|
||||
trace = attempt_command(
|
||||
args.model, args.ollama_url, prompt,
|
||||
args.rcon_host, args.rcon_port, args.rcon_pass,
|
||||
max_retries=args.max_retries,
|
||||
)
|
||||
|
||||
if trace["final_success"] and not trace["self_corrected"]:
|
||||
stats["success_first_try"] += 1
|
||||
stats["by_category"][cat]["success"] += 1
|
||||
n_cmds = len(trace["attempts"][0].get("commands", []))
|
||||
print(f" OK ({n_cmds} cmds)")
|
||||
elif trace["self_corrected"]:
|
||||
stats["self_corrected"] += 1
|
||||
stats["by_category"][cat]["corrected"] += 1
|
||||
print(f" CORRECTED ({len(trace['attempts'])} attempts)")
|
||||
else:
|
||||
stats["failed"] += 1
|
||||
print(f" FAILED")
|
||||
|
||||
# Convert to training examples
|
||||
examples = trace_to_training(trace)
|
||||
all_examples.extend(examples)
|
||||
stats["training_examples"] += len(examples)
|
||||
|
||||
# Brief pause between attempts
|
||||
time.sleep(1)
|
||||
|
||||
stats["rounds"] += 1
|
||||
|
||||
# Save
|
||||
if not args.dry_run and all_examples:
|
||||
output_path = Path(args.output)
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_path, "a") as f:
|
||||
for ex in all_examples:
|
||||
f.write(json.dumps(ex, ensure_ascii=False) + "\n")
|
||||
|
||||
# Summary
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Self-play complete")
|
||||
print(f" Rounds: {stats['rounds']}")
|
||||
print(f" Prompts generated:{stats['prompts_generated']}")
|
||||
print(f" Attempts: {stats['attempts']}")
|
||||
print(f" Success (1st try):{stats['success_first_try']}")
|
||||
print(f" Self-corrected: {stats['self_corrected']}")
|
||||
print(f" Failed: {stats['failed']}")
|
||||
print(f" Training examples:{stats['training_examples']}")
|
||||
|
||||
if stats["by_category"]:
|
||||
print(f"\n By category:")
|
||||
for cat, s in sorted(stats["by_category"].items()):
|
||||
total = s["total"]
|
||||
ok = s["success"]
|
||||
corr = s["corrected"]
|
||||
fail = total - ok - corr
|
||||
print(f" {cat:25} total={total} ok={ok} corrected={corr} failed={fail}")
|
||||
|
||||
print(f"\n Output: {args.output}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user