REVES: REvision and VErification--Augmented Training for Test-Time Scaling

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Software Development & Engineering · Depth: Expert, quick

Summary

REVES, a novel two-stage iterative framework, enhances Large Language Model (LLM) reasoning by addressing the misalignment between single-shot optimization and multi-step inference in test-time scaling via sequential revision. It alternates between online data/prompt augmentation and policy optimization, specifically converting "near-miss" intermediate steps from successful recovery trajectories into decoupled revision and verification prompts. This approach focuses training on both effective answer transformation and error identification, enabling efficient off-policy data generation and reducing computational overhead compared to standard multi-turn reinforcement learning. On LiveCodeBench, REVES achieved gains of +6.5 points over the RL baseline and +4.0 points over standard multi-turn training using publicly available test cases. It also matched the previously reported SOTA result on circle packing with a 4B base model and fewer rollouts, and demonstrated improved correction ability in math and generalization to n_queens and mini_sudoku puzzles.

Key takeaway

For Machine Learning Engineers developing LLMs for complex, multi-step reasoning tasks, you should consider REVES's iterative revision and verification-augmented training. This framework offers a significant performance boost, achieving +6.5 points over RL baselines on LiveCodeBench, while reducing computational overhead compared to standard multi-turn reinforcement learning. Integrating this approach can improve your model's ability to correct errors and generalize across diverse problem types, from coding to constraint-satisfaction puzzles.

Key insights

REVES enhances LLM multi-step reasoning by iteratively training on "near-miss" corrections and verifications, outperforming standard RL.

Principles

• Optimize for multi-step inference dynamics.
• Learn from intermediate "near-miss" mistakes.
• Decouple revision and verification prompts.

Method

REVES employs a two-stage iterative framework that alternates online data/prompt augmentation with policy optimization. It converts intermediate "near-miss" answers from successful recoveries into decoupled revision and verification prompts for targeted training.

In practice

• Utilize public test cases for feedback.
• Apply to coding and math reasoning tasks.
• Explore for constraint-satisfaction puzzles.

Topics

• REVES
• Large Language Models
• Test-Time Scaling
• Sequential Revision
• Reinforcement Learning
• Prompt Augmentation
• Error Correction

Code references

• yxliu02/REVES

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.