arXiv:2605.27788 · v2
Knowing When to Ask: Segment-Level Credit Assignment for LLM Tool Use
摘要
Humans know when to reach for help e.g. $347 \times 28$ warrants a calculator while $2+2$ does not. Language models do not. Prompt-based approaches can instruct a model when to invoke tools, but this scaffolding does not teach it to recognize the boundary of its own knowledge. RL approaches that assign a single outcome reward to the whole trajectory fare no better: trajectory-level credit cannot isolate which tool call in a successful episode actually helped, nor penalize unnecessary calls. We propose CARL (Competence-Aware Reinforcement Learning), which trains a critic on the model's own rollouts to learn where parametric knowledge suffices and where it needs external help. By decomposing each rollout at natural tool-use boundaries (e.g., code fence delimiters and context block transitions), CARL assigns independent credit to each segment from a single binary outcome, without external judges or step-level annotations. As a result, erroneous tool calls, incorrect extractions, and unnecessary calls each receive appropriately signed advantages. The trained critic captures the model's domain competence: it separates parametrically solvable from tool-dependent questions with AUC 0.93 at 7B. On five benchmarks spanning arithmetic, multi-hop factual QA, and numerical reasoning over financial tables, CARL improves average exact-match accuracy on the three search benchmarks by 6.7 points at 7B and 9.6 points at 3B over the best RL baseline, with the largest gain (+8.3 EM at 7B, +12.5 EM at 3B) on MuSiQue. At 7B, CARL has a 53% lower tool-use rate than Search-R1 PPO on parametrically answerable questions while remaining 9.8 EM points more accurate on them. Gains are largest at small scale: the 3B improvement is $1.4\times$ the 7B improvement, suggesting that knowing when to ask disproportionately benefits models with smaller parametric memory.
作者备注：Accepted at NeurIPS 2026 (main track, spotlight). 44 pages including appendices. OpenReview: https://openreview.net/forum?id=rzZuYaw8zp