Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models
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Computer Science > Computation and Language
Title:Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models
Abstract:While modern large reasoning models (LRMs) excel at providing correct answers in many tasks, we provide additional evidence for the observation that they often struggle with a critical capability: knowing when to abstain from answering. We analyze this gap by comparing LRM behavior to results from a human study, revealing that human reasoning effort on unanswerable tasks is upper-bounded by answerable tasks, whereas LRMs waste computational resources by generating longer Chains of Thought (CoTs) on unanswerable than on answerable prompts. To overcome this inefficiency, we take inspiration from a resource-rational perspective on human cognition and introduce a novel GRPO reward that encourages efficient reasoning about whether the task contains all the information needed to solve it. Fine-tuning several 4B LRMs with this reward leads to human-like abstention performance gains (+12.8% on average) while retaining answering capabilities and boosting the models' efficiency (44% shorter CoTs on average).
| Comments: | 19 pages, 9 figures |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.20846 [cs.CL] |
| (or arXiv:2609.20846v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20846
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