Predictive Divergence Masks for LLM RL

Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates. The dominant PPO-style approach uses the sampled-token importance ratio for two criteria: a proximity criterion, which asks whether the policy has moved too far from the behavior policy, and a direction criterion, which asks whether the update pushes it farther away. Recent work DPPO improves the proximity criterion by replacing PPO's ratio-based test with a probability

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ethics.ai (11 July 2026), “Predictive Divergence Masks for LLM RL,” evidence record 13020, https://ethics.ai/record/13020 (originally published by HuggingFace Daily Papers).

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