Agent RL stops relying on its own luck: failed rollouts get corrected mid-sampling, lifting navigation pass rate from 35% to 57%
Alibaba researchers introduce Rollout-Time Intervention, which corrects the decisive error step using reward-side rubrics whenever an entire rollout group fails, letting agent RL learn from successful trajectories the policy could not explore on its own and raising Navigation Pass@1 from GRPO's 35.33 to 57.33.