Small open-weight models match GPT-5 on routine agent tool-use tasks but lag on long-horizon planning, supporting tiered routing to reduce costs in agentic systems.
arXiv preprint arXiv:2501.10132 , year=
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AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
Audio2Tool is a new benchmark dataset that shows speech models perform well on simple commands but degrade sharply on compositional tasks and realistic acoustic noise.
A constrained-synthesis RL method with graduated rewards for atomic validity and orchestration consistency improves LLM turn accuracy on multi-step tool benchmarks and transfers to new API sets.
citing papers explorer
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AgentFloor: How Far Up the tool use Ladder Can Small Open-Weight Models Go?
Small open-weight models match GPT-5 on routine agent tool-use tasks but lag on long-horizon planning, supporting tiered routing to reduce costs in agentic systems.
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AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
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Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use
Audio2Tool is a new benchmark dataset that shows speech models perform well on simple commands but degrade sharply on compositional tasks and realistic acoustic noise.
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Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards
A constrained-synthesis RL method with graduated rewards for atomic validity and orchestration consistency improves LLM turn accuracy on multi-step tool benchmarks and transfers to new API sets.