Researchers propose a simple technique for stabilizing reinforcement learning training in PPO algorithms by randomly dropping 25% of transitions during rollouts. The method removes gradient redundancy caused by causally-dependent state sequences, improving training consistency across multiple environments without algorithmic modifications.
This research addresses a fundamental inefficiency in on-policy reinforcement learning that has largely gone unexamined. When agents collect sequential experience, consecutive transitions contain overlapping information—each state causally depends on the previous one. This creates repetitive gradient signals that reinforce the same parameter directions repeatedly, leading to unstable training dynamics that standard reward curves often fail to expose. The proposed solution is elegantly minimal: selectively dropping transitions breaks this redundant structure while preserving the reward signal integrity.
The technique represents a shift in thinking about batch efficiency in deep RL. Rather than collecting more data or using larger batches, the authors recognize that some data actively harms training through gradient repetition. This insight extends beyond PPO and could apply to other on-policy methods. The empirical validation across increasingly complex environments—from CartPole to Hopper—demonstrates consistent improvements in policy entropy, KL divergence stability, and value estimation quality, not just reward maximization.
For practitioners, this work provides an immediate, zero-cost optimization that integrates seamlessly with existing PPO implementations. The 25% dropout rate appears robust across different domains, suggesting the approach generalizes well. The stability improvements matter significantly for real-world deployment where erratic training dynamics can mask performance degradation. This finding could influence how researchers design and interpret RL experiments, potentially revealing that many existing instability issues stem from unrecognized gradient redundancy rather than fundamental algorithmic problems. The work suggests that simpler, more efficient training procedures may be achievable through better understanding of data dependencies rather than architectural innovations.
- →Random transition dropout at 25% rate stabilizes PPO training by eliminating gradient redundancy from causally-dependent states
- →The method requires only one sampling step with no algorithmic modifications and works on any existing PPO implementation
- →Training consistency improves across multiple metrics including KL divergence, policy entropy, and value estimates while maintaining reward performance
- →Consecutive transitions in on-policy RL carry overlapping information that creates repetitive gradients, a problem previously underexplored in the literature
- →The technique demonstrates that data efficiency gains come from removing harmful redundancy rather than collecting more experience