arXiv:2606.22600v2 Announce Type: replace-cross Abstract: On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers. In the standard KL objective of OPD, token-level losses are uniformly averaged, implying equal weights for all tokens. However, we discover that not all tokens are created equal: as student rollouts grow longer, they deviate further from the teacher's distribution, leading to degraded supervision quality at later positions. As a result, OPD using only the first 30% of tokens can perform c
This paper addresses a fundamental limitation in On-Policy Distillation (OPD), a technique critical for improving the efficiency of reinforcement learning, by identifying a 'position bias' that degrades its performance.
Improving the learning efficiency of reinforcement learning through techniques like OPD is crucial for making AI systems more powerful and accessible, directly impacting the development of advanced AI agents and potentially complex robotic systems.
This research suggests that current OPD methods may be suboptimal; by understanding and compensating for position bias, future RL training could become significantly more efficient, leading to faster progress in complex AI tasks.
- · AI researchers
- · Developers of AI agents
- · Reinforcement learning platforms
- · Companies using RL for complex control tasks
- · Inefficient RL training pipelines
More efficient and effective On-Policy Distillation methods will be developed, improving the training of reinforcement learning models.
This efficiency gain could accelerate the capabilities and deployment of AI agents in various applications, from industrial automation to complex decision-making.
Increased efficiency in RL could contribute to the development of more capable and cost-effective autonomous systems, expanding the reach of AI into new domains.
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Read at arXiv cs.AI