PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost
Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers from out-of-domain (OOD) degradation. Conversely, end-to-end reinforcement learning (E2E RL) preserves OOD capabilities, but incurs high compute costs due to many turns of on-policy rollout. We introduce PivotRL, a novel framework that operates on existing SFT trajectories to combine the compute efficiency of SFT with the OOD accuracy of E2E RL. PivotRL relies on two key mechanisms: first, it executes local, on-policy rollouts and filters for pivots: informative intermediate turns where sampled actions exhibit h...
| Authors | Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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