Reinforcement Learning with Decomposed Subtasks
Group Relative Policy Optimization (GRPO) and related policy-gradient methods for training language model agents collapse an entire multi-turn rollout into a single scalar trajectory reward before it enters the policy update. When the task composes distinct skills, especially under sparse and delayed environmental feedback, this collapsing is lossy: the optimizer must implicitly infer which competency drove the outcome and how that should change behavior. We argue the right primitive is not a better scalar but a decomposition: trajectory reward should be split along subtasks before it enters the policy update. We introduce Reinforcement Learning with Decomposed Subtasks (RLDS), whose core...
| Authors | Mattie Terzolo, Mikolaj Sacha, Ayan Sinha et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 September 2026 |
| Last updated at source | 24 September 2026 |
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