# AtomicRL: An Atomic Reinforcement Learning Post-Training Framework for Long-Horizon Robotic Manipulation

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/tt023mtmmyer78dllqi2d6mqb
- Original: https://arxiv.org/abs/2610.30097

## Summary

The paper proposes AtomicRL, a framework that uses atomic task structure to improve reinforcement learning post-training of vision-language-action (VLA) policies. Its core components include Semantic-Aware Policy Optimization (SAPO), which uses semantic gating to prevent TD residuals from failed atomic subtasks from propagating across tasks, thereby mitigating credit contamination; and Bottleneck-Adaptive Target Exploration (BATE), which dynamically coordinates task sampling and local exploration incentives based on the mastery level of each subtask.