aiqbench analysis
This paper introduces IGRPO, a reinforcement learning framework that optimizes computational budgets in LLM search agents by prioritizing trajectory branches based on their information gain. By dynamically allocating resources to more informative paths, the method improves accuracy on multi-turn search tasks without increasing overall compute costs. The work advances efficient training methodologies for agentic systems, offering a principled approach to managing exploration-exploitation trade-offs in complex decision-making environments.
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn Search Agents
arXiv:2607.06223v2 Announce Type: replace Abstract: Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome. However, rollout budgets are often allocated without assessing intermediate-state utility, which can waste computation on unpromising branches. We propose Information Gain-based Rollout Policy Optimization (IGRPO), a framework that organizes rollout collection around intermediate-state informativeness. Specifically, IGRPO performs budget-aware tree-structured rollouts in which expansion probabilities depend on node-level informativeness, allowing informative branches to receive more computation while less informative branches are expanded less frequently within a fixed rollout budget. By directly shaping how training trajectories are generated, IGRPO induces a limiting teacher distribution over search trajectories that favors higher cumulative informativeness. The resulting distribution provides an explicit policy optimization target, connecting adaptive rollout collection with principled policy learning. Experiments on seven search-augmented question answering benchmarks show that IGRPO achieves higher average accuracy than strong baselines on both 3B and 7B backbones under comparable rollout budgets, supporting informativeness-guided trajectory generation for training search agents. Code is available at https://github.com/e3trange/IGRPO.
Abstract:Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome. However, rollout budgets are often allocated without assessing intermediate-state utility, which can waste computation on unpromising branches. We propose Information Gain-based Rollout Policy Optimization (IGRPO), a framework that organizes rollout collection around intermediate-state informativeness. Specifically, IGRPO performs budget-aware tree-structured rollouts in which expansion probabilities depend on node-level informativeness, allowing informative branches to receive more computation while less informative branches are expanded less frequently within a fixed rollout budget. By directly shaping how training trajectories are generated, IGRPO induces a limiting teacher distribution over search trajectories that favors higher cumulative informativeness. The resulting distribution provides an explicit policy optimization target, connecting adaptive rollout collection with principled policy learning. Experiments on seven search-augmented question answering benchmarks show that IGRPO achieves higher average accuracy than strong baselines on both 3B and 7B backbones under comparable rollout budgets, supporting informativeness-guided trajectory generation for training search agents. Code is available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2607.06223 [cs.AI]
(or arXiv:2607.06223v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.06223 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 263 words
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