AI

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 14:18:24 UTC.

Briefings

A new framework called Rationale-Guided Policy Optimization (RGPO) improves large language model reasoning by using ground-truth rationales as temporary scaffolds during training. This approach reduces reward sparsity by allowing models to generate improved responses before transferring higher-reward solutions back to the original setting. The method consistently outperforms existing RLVR baselines across both language-only and vision-language reasoning settings, demonstrating its effectiveness in stabilizing reinforcement learning and improving performance in both text-only and multimodal models.

Key points

• RGPO uses ground-truth rationales as temporary scaffolds to help models generate improved responses before transferring higher-reward solutions back to the original setting.
• The framework consistently improves performance over RLVR baselines in both language-only and vision-language reasoning settings.

Context

On-policy reinforcement learning is a central paradigm for improving large language model reasoning, but it struggles with reward sparsity. Existing methods often require auxiliary data matching the RL task format, which is difficult to obtain. RGPO addresses this by leveraging ground-truth rationales adaptively while preserving model freedom to explore.

Limitations

The study covers both language-only and vision-language reasoning settings but does not specify the exact hardware or software environments used. Results are based on ablation studies and do not include long-term deployment data or real-world evaluation metrics.

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