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@omarsar0

NEW research from NVIDIA. Post-training agents with RL is powerful but expensive. Every parameter update needs full multi-turn rollouts with environment interactions, making end-to-end RL prohibitively costly for long-horizon agentic tasks. This research offers a practical middle ground. The work introduces PivotRL, a framework that operates on existing SFT trajectories to combine the computational efficiency of SFT with the out-of-domain retention of end-to-end RL. Instead of exhaustive full-trajectory rollouts, PivotRL identifies pivots, informative intermediate turns where sampled actions show mixed outcomes, and trains only on those high-signal moments. Standard SFT degrades OOD performance by -9.83 points on average. PivotRL stays near zero (+0.21) while achieving +14.11 average in-domain gains over the base model versus +9.94 for SFT. On SWE-Bench, PivotRL reaches competitive accuracy with E2E RL using 4x fewer rollout turns and 5.5x less wall-clock time. The method is already deployed in production as the workhorse for NVIDIA's Nemotron-3-Super-120B agentic post-training. Paper: https://t.co/sIBLUpyfMD Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

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  "text": "NEW research from NVIDIA.\n\nPost-training agents with RL is powerful but expensive.\n\nEvery parameter update needs full multi-turn rollouts with environment interactions, making end-to-end RL prohibitively costly for long-horizon agentic tasks.\n\nThis research offers a practical middle ground.\n\nThe work introduces PivotRL, a framework that operates on existing SFT trajectories to combine the computational efficiency of SFT with the out-of-domain retention of end-to-end RL.\n\nInstead of exhaustive full-trajectory rollouts, PivotRL identifies pivots, informative intermediate turns where sampled actions show mixed outcomes, and trains only on those high-signal moments.\n\nStandard SFT degrades OOD performance by -9.83 points on average. PivotRL stays near zero (+0.21) while achieving +14.11 average in-domain gains over the base model versus +9.94 for SFT.\n\nOn SWE-Bench, PivotRL reaches competitive accuracy with E2E RL using 4x fewer rollout turns and 5.5x less wall-clock time.\n\nThe method is already deployed in production as the workhorse for NVIDIA's Nemotron-3-Super-120B agentic post-training.\n\nPaper: https://t.co/sIBLUpyfMD\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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