| """Collapse guard: the held-out, human-grounded eval gate. |
| |
| The whole self-refinement loop is dangerous precisely because the AI rates its own |
| training data -- that's how model collapse happens. This gate is the antidote: a FROZEN |
| eval set that the AI never generates or rates, scored by the judge, used only to decide |
| whether to KEEP a refinement round. If a round regresses on real held-out tasks, it's |
| reverted. This converts "spirals down" into "only changes that actually help survive." |
| """ |
| import json |
| from pathlib import Path |
|
|
|
|
| def load_eval(path): |
| p = Path(path) |
| if not p.exists(): |
| return [] |
| return [json.loads(l) for l in p.read_text(encoding="utf-8").splitlines() if l.strip()] |
|
|
|
|
| def evaluate(policy, tok, judge, eval_items): |
| """Mean judge score of the policy on the frozen eval set.""" |
| if not eval_items: |
| return 0.0 |
| from core.genutil import chat_generate |
| from core import modalities |
| total = 0.0 |
| for it in eval_items: |
| resp = chat_generate(policy, tok, [{"role": "user", "content": it["instruction"]}], |
| max_new_tokens=512, do_sample=False) |
| js = judge.score(it["instruction"], resp) |
| total += modalities.blended_reward(it.get("type"), js, resp) |
| return total / len(eval_items) |
|
|
|
|
| def keep_round(new_score, prev_score, tolerance=0.0): |
| """Keep the round only if it doesn't regress beyond tolerance.""" |
| return new_score >= (prev_score - tolerance) |
|
|
