Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
📰 ArXiv cs.AI
Learn to correct likelihood-ratio in reinforcement learning for verifiable rewards and improve policy gradient objectives
Action Steps
- Apply multi-step likelihood-ratio correction to PPO surrogate objectives
- Run experiments to evaluate the effect of correction on policy gradient objectives
- Configure the correction algorithm to minimize structural bias
- Test the performance of the corrected model on verifiable reward tasks
- Compare the results with traditional PPO methods to assess improvement
Who Needs to Know This
Researchers and engineers working on reinforcement learning and large language models can benefit from this technique to improve their models' reasoning abilities
Key Insight
💡 Multi-step likelihood-ratio correction can reduce structural bias in PPO surrogate objectives and improve policy gradient objectives
Share This
🤖 Improve RL with verifiable rewards using multi-step likelihood-ratio correction! 🚀
Key Takeaways
Learn to correct likelihood-ratio in reinforcement learning for verifiable rewards and improve policy gradient objectives
Full Article
Title: Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
Abstract:
arXiv:2605.20865v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely used PPO surrogate objectives are fundamentally local, as they rely on a local approximation of the exact policy gradient objective. While this approximation improves stability by reducing the variance induced by importance sampling, it also introduces structural bias into the surrogate objective,
Abstract:
arXiv:2605.20865v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely used PPO surrogate objectives are fundamentally local, as they rely on a local approximation of the exact policy gradient objective. While this approximation improves stability by reducing the variance induced by importance sampling, it also introduces structural bias into the surrogate objective,
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