aiqbench analysis
This research introduces DASH, a novel reinforcement learning method that assigns credit at the segment level to curb "overthinking" in reasoning language models without requiring expensive step-level annotations. By leveraging intermediate answer commitments as proxies for correctness, the approach significantly reduces unproductive self-reflection while improving accuracy on complex math benchmarks compared to standard GRPO baselines.
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking
arXiv:2607.00482v2 Announce Type: replace Abstract: Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction that consume tokens without improving answers. We show that these behaviors are not merely a consequence of length; even when controlling for response length, incorrect traces exhibit higher rates of unproductive self-reflection than correct ones. Addressing this requires identifying where self-reflection helps vs hurts, but obtaining these step-level annotations is costly. We observe that intermediate answer commitments within reasoning traces can provide a cheap proxy: by comparing each final answer candidate in the trace to the ground truth, we can determine whether subsequent reflection is productive without any additional supervision. Building on this insight, we propose DASH (Drift Aware advantage SHaping), which assigns segment-level credit based on whether each reasoning segment leads toward or away from correctness. On competition-level math benchmarks, DASH achieves the highest accuracy where overthinking is prevalent (Average Accuracy: 59.45% vs. 58.1% Dr.GRPO vs. 56.95% GRPO) while reducing overthinking behaviors and achieving more productive self-correction than baselines.
Abstract:Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction that consume tokens without improving answers. We show that these behaviors are not merely a consequence of length; even when controlling for response length, incorrect traces exhibit higher rates of unproductive self-reflection than correct ones. Addressing this requires identifying where self-reflection helps vs hurts, but obtaining these step-level annotations is costly. We observe that intermediate answer commitments within reasoning traces can provide a cheap proxy: by comparing each final answer candidate in the trace to the ground truth, we can determine whether subsequent reflection is productive without any additional supervision. Building on this insight, we propose DASH (Drift Aware advantage SHaping), which assigns segment-level credit based on whether each reasoning segment leads toward or away from correctness. On competition-level math benchmarks, DASH achieves the highest accuracy where overthinking is prevalent (Average Accuracy: 59.45% vs. 58.1% this http URL vs. 56.95% GRPO) while reducing overthinking behaviors and achieving more productive self-correction than baselines.
Computation and Language (cs.CL)
arXiv:2607.00482 [cs.CL]
(or arXiv:2607.00482v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.00482 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 263 words
