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ccbd209 9bf4115 ccbd209 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | """Critique -> revise: the model learns from criticism.
For each prompt: the policy answers, the judge writes a criticism, the policy REVISES
using that criticism, and if the revision scores higher it becomes a training pair
(prompt -> revised). This is how "take criticism into consideration" becomes signal.
"""
def _gen(model, tok, messages, max_new_tokens=512):
from core.genutil import chat_generate
return chat_generate(model, tok, messages, max_new_tokens=max_new_tokens,
do_sample=True, temperature=0.7, top_p=0.95)
def critique_revise_batch(policy, tok, judge, prompts):
"""Returns improved (instruction, response) pairs where revision beat the original."""
pairs = []
for p in prompts:
first = _gen(policy, tok, [{"role": "user", "content": p}])
verdict = judge.critique(p, first)
if verdict["score"] >= 0.9:
pairs.append({"instruction": p, "response": first, "score": verdict["score"]})
continue
revise_msgs = [
{"role": "user", "content": p},
{"role": "assistant", "content": first},
{"role": "user", "content": f"Criticism: {verdict['criticism']} "
f"(weakest: {verdict['weakest']}). Revise to fix this."},
]
revised = _gen(policy, tok, revise_msgs)
rscore = judge.score(p, revised)
best, bscore = (revised, rscore) if rscore > verdict["score"] else (first, verdict["score"])
pairs.append({"instruction": p, "response": best, "score": bscore})
return pairs
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