subtask-decomposition

This paper introduces Reinforcement Learning with Decomposed Subtasks (RLDS), a method that decomposes trajectory reward into per-subtask advantages to improve credit assignment in reinforcement learning for language model agents, showing significant gains on high-heterogeneity agentic benchmarks.