Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
Reinforcement learning has become a promising paradigm for improving large language model (LLM) agents on long-horizon search tasks, where the agent must make a sequence of intermediate decisions before receiving a final outcome. However, existing methods still face a key limitation: the rollout budget is often allocated without explicitly assessing the utility of intermediate states. As a result, substantial computation may be spent on low-value states, even though different branches can vary drastically in their informativeness. In this paper, we propose Information Gain-based Rollout Policy
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- PossiblePossibly related (embedding) · 47%rllm-org/rllm →
- PossiblePossibly related (embedding) · 47%Best practices for multi-turn reinforcement learning in Amazon SageMaker AI →
- LinkedLinked via arxiv author · 85%Yijun Zhang →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”
- LinkedLinked via arxiv author · 85%Tianfan Xue →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”
- LinkedLinked via arxiv author · 85%Jiaxin Ding →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”
- LinkedLinked via arxiv author · 85%Yule Xie →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”
- LinkedLinked via arxiv author · 85%Shiqing Gao →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”
- LinkedLinked via arxiv author · 85%Haoxiang Zhang →
“Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agen”