REVES: Training Method for Revisionable Reasoning
zhaoran_wang · x · 2026-07-11
This thread introduces the paper REVES (REvision and VErification–Augmented Training for Test-Time Scaling), aiming to bridge the gap between training methods and real-world deployment. The authors point out that LLMs rarely solve difficult tasks in a single try:
- Coding agents run code, read tracebacks, and patch errors.
- Math solvers check answers before proceeding to the next reasoning step.
However, traditional training typically optimizes for a single forward pass and a single reward, assuming single-step outputs while ignoring the "revisionable, verifiable, and iterable" processes used in actual deployment. REVES addresses this by incorporating revision and verification into training, better adapting models to test-time multi-turn scaling.
Related event: REVES Trains Models for Revision Loops(3 posts)→
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