Self-Trained Verification for Training- and Test-Time Self-Improvement
Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; and at training time, through self-training methods. Both are gated by the same bottleneck: the verifier. V-R loops stall when verifier scores inflate while accuracy stagnates, and when feedback is too generic to act on; self-training fails similarly when bad self-generated data are added to training. Better verification would unlock both, but the capability we want to train, i.e., catching self-generated errors, lacks training signal. To address this challenge, we propose self-trained verification (STV). Our key...
| Authors | Chen Henry Wu, Aditi Raghunathan |
|---|---|
| Primary category | cs.LG |
| Published | 28 May 2026 |
| Last updated at source | 31 May 2026 |
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