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AutoTool: Automatic Scaling of Tool-Use Capabilities in RL via Decoupled Entropy Constraints

ICLR 2026 · Large language models › Reasoning and chain of thought · approach: RL with verifiable rewards for reasoning · 0 citations (OpenAlex)

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Proposes AutoTool, an SFT warm-up followed by GRPO-style RL with decoupled adaptive entropy constraints that lets a Qwen2.5-7B tool-use model automatically choose between think and no-think modes, improving BFCL accuracy while cutting tokens.

Problem

Direct RL for tool use suffers reasoning collapse (shrinking thinking length), while scaled-up distilled reasoning models overthink simple tool-use queries at high token cost.

Approach

Warm-up SFT on mixed think/no-think data, then RL with an entropy loss decoupled by trajectory type: fixed entropy coefficient for short responses and an adaptively updated coefficient for long reasoning to keep entropy above a target, plus an asymmetric mode-dependent answer reward.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: ToolACE xLAM APIGen-MT-5k Hermes Function-Calling PubTool

Benchmarks: BFCL API-Bank ACEBench

Metrics: accuracy overall accuracy thinking rate token cost ACU

Baselines: Qwen2.5-7B-Instruct PubTool-SFT PubTool-Distilled PubTool-GRPO Thinkless AdaCtrl ToolRL-7B Tool-N1-7B Qwen3-8B GPT-4o o3 Gemini-2.5-Pro

Builds on: GRPO ToolACE Thinkless AdaCtrl

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