AI

VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 03:18:25 UTC.

Briefings

VeriFine is an agent harness framework designed to enable continuous self-improvement in embodied reasoning by scaling verification alongside policy and judge capabilities. The framework operates through two loops: the Policy Improvement Loop uses a rubric judge to diagnose failures and optimize the policy, while the Judge Improvement Loop refines the judge via human-guided calibration when progress plateaus. Experiments on driving and robot navigation tasks confirm that scaling verification supports continuous self-improvement as failure patterns evolve.

Key points

• VeriFine introduces a dual-loop architecture where the Policy Improvement Loop diagnoses recurring failures using a rubric judge to optimize the policy, and the Judge Improvement Loop refines the judge through human-guided calibration when optimization stalls.
• The framework operates across reinforcement and supervised fine-tuning tasks, demonstrating that scaling verification enables continuous self-improvement in both policy and judge capabilities on driving and robot navigation.

Context

Embodied reasoning tasks require agents to handle spatial grounding, causal reasoning, and safety-aware decisions, making evaluation challenging. Current fixed judges constrain optimization feedback and example discovery, limiting self-improvement. VeriFine addresses this by scaling verification through co-evolution of policy, curriculum, and judge.

Limitations

Evidence covers driving and robot navigation tasks only. Results are from experiments using reinforcement and supervised fine-tuning methods. No specific dates, author names, or quantitative metrics beyond 'continuous self-improvement' are provided.

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