REVES Aligns Training Objectives with Deployment Loops
zhaoran_wang · x · 2026-07-11
The author thanks the team for advancing their new work, REVES. The core insight is that while we often deploy LLMs in loops—iteratively revising, searching, and evolving—we still optimize them during training as if they were for a "single forward pass." REVES aims to change this by directly aligning training objectives with real-world deployment goals.
The paper argues that once this alignment is established, the benefits extend beyond a single task to an entire class of revision-style harnesses: any usage centered on "generate first, then iteratively rewrite/retrieve/evolve." The author stresses that the payoff from training-deployment consistency happens at the "family level," rather than being limited to a specific template.
Related event: REVES Trains Models for Revision Loops(3 posts)→
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