AI BriefingEntropy-Preserving Reinforcement Learning
Key point
Controlling the entropy-reducing tendency of policy gradient training preserves exploration diversity.
Details
Policy gradient-based training has boosted language model reasoning performance, but as training progresses, entropy tends to decrease, reducing exploration diversity. As a result, the policy increasingly explores only a limited set of trajectories, weakening its ability to learn in new environments.
The authors argue that entropy should be monitored and controlled throughout the training process. To this end, they theoretically analyze how major policy gradient objective functions affect entropy changes, and also point out that experimental factors such as numerical precision significantly influence entropy behavior.
As solutions, they propose the REPO family of algorithms, which modify the advantage to regulate entropy, and ADAPO, which adaptively applies asymmetric clipping. Models trained with these methods maintained diversity better throughout training, achieved better final performance, and showed policies that were better preserved in subsequent sequential training.
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