Hugging Face Trending Papers
Settle: Learning When to Stop Reasoning
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arXiv Computation and Language
Oct 1
Settle: Learning When to Stop Reasoning
arXiv:2609.38997v1 Announce Type: new Abstract: Reasoning models often continue generating after their answers have settled. Settle learns when to stop from answer stability in completed traces. It t...
arXiv AI
Aug 17
Early Stopping for Large Reasoning Models via Confidence Dynamics
arXiv:2604. 04930v2 Announce Type: replace-cross Abstract: Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade performance due to overthinking.
arXiv AI
Jul 1
When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models
arXiv:2606. 30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simple confidence or convergence thresholds.
arXiv Computation and Language
Aug 28
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
TRACES (Tagging Reasoning Steps for Adaptive Cost‑Efficient Early‑Stopping) is a lightweight framework that tags reasoning steps of large‑language models in real time, enabling adaptive, cost‑efficient early stopping during inference. By monitoring the types of steps generated, the method identifies when models shift their reasoning after arriving at a correct answer, allowing for interpretable stopping criteria. Experiments on mathematical reasoning benchmarks (MATH500, GSM8K, AIME) and knowledge benchmarks (MMLU, GPQA) show token reductions of 20–50% while preserving accuracy, with more conservative thresholds needed for harder tasks such as BeyondAIME and IMO AnswerBench.
arXiv Computation and Language
Oct 21
Adaptive Stopping of Streamed Reasoning for Language Model Agents
The paper introduces StreamStop, a lightweight stopping policy for reasoning-enabled language model agents that learns offline from prefix-level action agreement. By encoding available reasoning text with a frozen prefix reader and a stop head, StreamStop can decide when to stop reasoning without needing internal signals from the model. Experiments on WebShop and ScienceWorld show that StreamStop achieves higher success rates than many fixed-budget baselines while using fewer reasoning tokens, notably reducing token usage by 42% on WebShop with Qwen3.8-27B.
arXiv AI
Aug 12
TrAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLMs
arXiv:2608. 00422v2 Announce Type: replace Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty estimation important for abstention, human review, and adaptive compute allocation.