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Improving Sparse-Reward RL for Vision-Language Math Reasoning with Prior Injection.

Qiqian Fu· August 25, 2026 View original

Key takeaways

  • Sparse rewards in vision-language math reasoning hinder RL agent training.
  • Effective prior injection, like hints or teacher distillation, significantly improves performance.
  • Traditional in-domain evaluation metrics can be misleading for cross-domain transfer.
  • Hint-guided exploration and specific loss functions (HL-Gauss) offer notable improvements.

Who benefits

EdTechAI-EngineeringRoboticsAutomotiveHealthcare

Summary

This research explores methods to overcome sparse reward challenges in reinforcement learning for vision-language math reasoning by injecting various priors like text hints, distribution distillation, and value-pretrained critics. It finds that effective prior delivery significantly boosts performance and identifies critical evaluation metrics for cross-domain transfer.

Reinforcement learning (RL) models often struggle with vision-language math reasoning tasks due to sparse rewards, where most attempts yield no useful feedback. This paper investigates eleven different methods of "prior injection" to address this, including providing textual hints, distilling knowledge from a more capable teacher model, and using a pre-trained value critic. The study reveals that the timing and effectiveness of prior delivery are crucial; methods where the prior successfully influenced the policy showed marked improvement. A key finding also emerged regarding evaluation: traditional in-domain metrics can be misleading. A specific subset of the in-domain problems, previously used for general distribution checks, actually anti-correlated with true cross-domain transfer performance. Conversely, the most challenging in-domain problems proved to be strong predictors of cross-domain success. Among the successful techniques, hint-guided exploration was more impactful than auxiliary losses, and replacing the critic's MSE loss with HL-Gauss cross-entropy yielded substantial gains.

Why it matters

Professionals developing AI agents for complex, multi-modal tasks, especially those with infrequent positive feedback, can leverage these findings to design more robust and efficient training strategies.

How to implement this in your domain

  1. 1Integrate hint-guided exploration into sparse-reward RL systems for vision-language tasks.
  2. 2Experiment with different prior injection methods, such as knowledge distillation from larger models.
  3. 3Adopt HL-Gauss cross-entropy loss for value critics instead of MSE to improve performance.
  4. 4Re-evaluate existing in-domain benchmarks to ensure they accurately predict cross-domain transfer capabilities.

Original post by Qiqian Fu

"arXiv:2608.21811v1 Announce Type: new Abstract: Reinforcement learning for vision-language math reasoning starves under sparse reward: on a pool of 20,830 visual-math problems where Qwen2-VL-2B answers 3.6% of rollouts correctly, 85-97% of GRPO rollout groups are entirely wrong a…"

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