ARXIV:2606.03762 · AGENTIC REINFORCEMENT LEARNING · SUBMITTED 03 JUN · 20:32 UTC · FRESHNESS FRESH

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Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

Hongye Cao · Nuo Yan · Haoyuan Deng · Ziwei Wang · Tianpei Yang · Jing Huo · +2 at arXiv

A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

Ship in 2-4 weeks›Score7.0Evidence partial

Opportunity summary

Pain A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

Evidence 0 refs | 4 sources | 83% coverage

Blocker Evidence partial

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PROBLEM

A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration. However, integrating external tools often destabilizes training: over-reliance on tools can induce input distribution shift, while overly…

METHOD

Full abstract

Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating external tools often destabilizes training: over-reliance on tools can induce input distribution shift, while overly conservative tool use limits effective exploration. To address this issue, we propose a unified framework TAO-RL that couples tool-aware trajectory filtering with entropy-guided exploration for efficient policy optimization. Specifically, at the data level, TAO-RL filters rollout trajectories along two criteria: discarding those where all tool invocations fail to execute, and removing those where all rollouts are either correct or incorrect, as both cases yield degenerate advantage estimates that contribute no discriminative learning signal. This joint filtering retains data that are both tool-capable and informative, establishing a high-quality training distribution. At the algorithmic level, we introduce a tool-aware entropy-guided bonus that reshapes the advantage function at post-tool-call tokens, encouraging the policy to explore more diverse reasoning paths at critical decision points. These two components are mutually reinforcing: trajectory filtering establishes a clean and informative training foundation, while entropy-guided exploration drives stronger reasoning behaviors at critical tool-interaction junctures. Extensive experiments on 7 challenging reasoning benchmarks across 3 model scales demonstrate the superiority of TAO-RL over existing methods.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. A public repository is linked,…

WHY NOW

Agentic Reinforcement Learning moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

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Opportunity summary

Score7.0

PainA framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

Evidence0 refs | 4 sources | 83% coverage

Blockerno shell-level blocker reported

Analysis summary

A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: partial proof status

Competitive landscape

A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

Segment

Agentic Reinforcement Learning

Adoption evidence

Public code linked for build inspection

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A framework for agentic reinforcement learning that improves LLM tool use by filtering trajectories and guiding exploration.

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Agentic Reinforcement Learning

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Public code linked for build inspection

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7.0/10 public viability

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not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

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