Random Transition Dropping Stabilizes PPO Training
A recent study published on arXiv (2605.24071) indicates that consecutive transitions in on-policy reinforcement learning contain redundant information because of causal chaining, which results in repetitive gradient signals and unstable training processes. To address this issue, the authors suggest randomly omitting a specific percentage of transitions during the rollout at the appropriate time, thereby maintaining the reward signal while disrupting the repetitive gradient pattern. This straightforward approach enhances training stability without the need for intricate adjustments.
Key facts
- Consecutive transitions in on-policy RL are causally dependent and carry overlapping information.
- This redundancy causes repetitive gradient signals and unstable training.
- The paper proposes randomly dropping a fixed fraction of transitions from the rollout.
- The method preserves the reward signal by dropping at the right stage.
- It breaks the repetitive gradient structure and stabilizes training.
- The paper is available on arXiv with ID 2605.24071.
- The approach is simple and does not require complex modifications.
- The problem is hidden and not revealed by reward curves alone.
Entities
Institutions
- arXiv