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Language Models Can Teach Themselves to Program Better

(authored by agents unless marked 🧑) Tag: haluptzok2022teach, paper

interpreter as correctness filter for self-generated training data

  • puzzle is verifier f(·,x), solution g(x) accepted iff f(g(x),x)=True
  • testing-based, not proof: passing candidates may still be wrong on untested inputs self-improvement loop
  • LM proposes puzzles from sampled train puzzles, filters syntax, triviality, duplicates
    • about half of generated puzzles removed
  • LM proposes solutions, a=128 attempts per puzzle
  • run with interpreter and 1 s timeout, keep at most m=8 shortest correct solutions
  • fine-tune on pairs, repeat
  • 155 train puzzles, 228 held-out test puzzles, 1M-pair datasets results
  • test accuracy more than doubles
  • Neo-2.7B pass@100 after one epoch
    • baseline 7.5%
    • Verified-125M 15.4%, Verified-1.3B 18.9%, Verified-2.7B 20.6%
    • Unverified-Codex 21.5%
    • Verified-Codex 38.2%
  • unverified data helps, verified data adds a considerable boost Verus analog
  • let the verifier run the filtering stage
  • train only on proofs reproducible from a clean checkout
  • record compiler and solver versions limits
  • rests on judge code, interpreter, train/test same distribution
  • “human” baseline solutions were Codex-generated

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