Partial Policy Gradients for RL in LLMs

Reinforcement learning is a framework for learning to act sequentially in an unknown environment. We propose a natural approach for modeling policy structure in policy gradients. The key idea is to optimize for a subset of future rewards: smaller subsets represent simpler policies, which can be learned more reliably because their empirical gradient estimates are more accurate. Our approach allows for modeling and comparison of different policy classes, including full planning, greedy, K-step loo

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ethics.ai (6 March 2026), “Partial Policy Gradients for RL in LLMs,” evidence record 7607, https://ethics.ai/record/7607 (originally published by arXiv).

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