Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits
Rollout-Time Intervention supplies off-policy successes so LLM agents learn beyond their current policy.
Alibaba researchers introduce Rollout-Time Intervention (RTI) for reinforcement learning of large language model agents. When tasks exceed the current policy, successful on-policy trajectories are too rare and training collapses. RTI uses reward-side evaluation rubrics during rollout to turn failed trajectories into successes, then drops trajectories with excessive distributional shift before optimization. Across five benchmarks, RTI outperformed RL baselines and let the post-trained policy approach much stronger contemporary LLMs. Code is available at github.com/Alibaba-NLP/qqr.
- RTI repairs failed rollouts into successful learning signals.
- Trajectories with excessive distributional shift are filtered out.
- RTI beat RL baselines across five benchmarks.
- Code is released at Alibaba-NLP/qqr.
From the original article
1 min read at arxiv.orgReinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and tool-use in interactive environments.
By Chen Xi · arxiv.org
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