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arXiv AI By Amit Sagtani, Saloni Garg

Measuring the Marginal Value of Verifier-Based RL Beyond Supervised Fine-Tuning

Read the original on arXiv AI →

The paper evaluates the true benefit of verifier‑based reinforcement learning (RLVR) over supervised fine‑tuning (SFT) using a deterministic benchmark of 1,877 synthetic GPU and LLM training failure diagnosis tasks. In controlled experiments, SFT alone achieved high performance (0.785 on the hard split), while RLVR added no detectable improvement in‑distribution and only a modest gain under distribution shift (+0.33 points). The study concludes that most performance gains come from SFT, with verifier‑based RL contributing only small additional benefits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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