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arXiv CS.AI·Jun 9·5 min read
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
TL;DR
Reinforcement learning with verifiable rewards (RLVR) faces challenges due to binary outcome rewards, leading to issues like Zero-Advantage Collapse and Hallucinated Certainty. This study introduces a new approach to policy optimization that mitigates these failures by leveraging dense intrinsic signals.
✦ Why It Matters
Engineers can enhance AI model training by integrating dense intrinsic signals to improve reasoning and outcome reliability.
Key Takeaways
- Explore dense intrinsic signals for policy optimization.
- Avoid binary outcome rewards to prevent model collapse.
- Implement new methods to enhance reasoning capabilities.
- Measure task completion rates to evaluate model performance.
Full Summary
Reinforcement learning with verifiable rewards (RLVR) is a method that helps large language models perform long-chain reasoning by providing rewards that can be verified. However, existing techniques like Group Relative Policy Optimization (GRPO) suffer from two main issues: Zero-Advantage Collapse, where all attempts yield the same result, causing gradients to vanish, and Hallucinated Certainty, where the model becomes overly confident in incorrect outcomes.
To address these problems, a new policy optimization method utilizing dense intrinsic signals was developed. This approach allows for more nuanced feedback during training, leading to better differentiation in outcomes.
Experiments showed that models using this method achieved a significant increase in reasoning accuracy, with improvements measured in task completion rates. These findings suggest that incorporating dense intrinsic signals can enhance the robustness of reinforcement learning systems.
Such advancements are crucial for engineers and researchers aiming to develop more reliable AI systems.