REVES: Revision Capability Generalizes Across Tasks
zhaoran_wang · x · 2026-07-11
The key takeaway here is that while LLMs are often deployed in "loops" (iteratively revising, searching, and evolving), they are typically trained for single-pass inference. REVES proposes **directly aligning training objectives with deployment goals**, allowing a single training effort to benefit a whole class of revision-style harnesses. Replies further clarify that revision isn't a domain-specific quirk. When checkpoints trained exclusively on math and code were applied zero-shot to **n_queens** and **mini_sudoku**, REVES delivered the biggest improvements—without needing puzzle data or specific tuning. The author concludes this acts as a general skill: the model learns to review its own answers and self-correct, an ability that naturally transfers to untrained tasks.
Related event: REVES Trains Models for Revision Loops(3 posts)→
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