HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime
arXiv:2605.30201v1 Announce Type: new Abstract: We investigate a narrow but common failure mode of GRPO-style reinforcement learning in the context of sparse verifiable rewards: early updates contain more responses with negative advantages than those with positive advantages, while response-level length normalization ties the magnitude of the update to the length of the output. We propose Hysteretic Policy Optimization (HPO), a minimal modification of GRPO that reduces the weight of negative-advantage updates and replaces per-response length normalization with mean-length normalization. We fur
This research addresses a common failure mode in reinforcement learning, suggesting improvements that can enhance the efficiency and stability of training complex AI models.
Improved reinforcement learning techniques are critical for advancing AI agents and autonomous systems, particularly in situations with sparse rewards, which are common in real-world applications.
The proposed Hysteretic Policy Optimization (HPO) offers a more robust method for training intelligent systems, potentially accelerating the development and deployment of agentic AI.
- · AI developers
- · Robotics companies
- · Game development
- · Research institutions
- · Organizations reliant on inefficient RL algorithms
- · Competitors using less advanced optimization methods
More stable and efficient training of reinforcement learning models becomes possible.
This leads to faster development and deployment of advanced AI agents and autonomous systems across various industries.
It could accelerate the timeline for widespread adoption of agentic AI, transforming white-collar workflows and industrial automation.
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Read at arXiv cs.LG