Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

TL;DR

This study reveals that brief chain-of-thought reasoning significantly improves function-calling accuracy in language agents, while extended reasoning can impair performance, leading to the proposal of a structured brief-CoT method for better reliability.

Contribution

It uncovers the non-monotonic effects of reasoning length on agent accuracy and introduces Function-Routing CoT (FR-CoT), a structured approach that enhances reliability without budget tuning.

Findings

Brief reasoning (32 tokens) boosts accuracy by 45%.

Long reasoning (256 tokens) degrades performance below no-CoT baseline.

FR-CoT reduces hallucinations to 0% and maintains accuracy at brief reasoning levels.

Abstract

How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood. We present a systematic study of CoT budget effects on function-calling agents, sweeping six token budgets (0--512) across 200 tasks from the Berkeley Function Calling Leaderboard v3 Multiple benchmark. Our central finding is a striking non-monotonic pattern on Qwen2.5-1.5B-Instruct: brief reasoning (32 tokens) dramatically improves accuracy by 45% relative over direct answers, from 44.0% to 64.0%, while extended reasoning (256 tokens) degrades performance well below the no-CoT baseline, to 25.0% (McNemar p < 0.001). A three-way error decomposition reveals the mechanism. At d = 0, 30.5% of tasks fail because the model selects the wrong function from the candidate set; brief CoT reduces this to 1.5%, effectively acting as a function-routing step, while long CoT reverses the gain, yielding 28.0% wrong selections and 18.0% hallucinated functions at d = 256. Oracle analysis shows that 88.6% of solvable tasks require at most 32 reasoning tokens, with an average of 27.6 tokens, and a finer-grained sweep indicates that the true optimum lies at 8--16 tokens. Motivated by this routing effect, we propose Function-Routing CoT (FR-CoT), a structured brief-CoT method that templates the reasoning phase as "Function: [name] / Key args: [...]," forcing commitment to a valid function name at the start of reasoning. FR-CoT achieves accuracy statistically equivalent to free-form d = 32 CoT while reducing function hallucination to 0.0%, providing a structural reliability guarantee without budget tuning.