Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of-thought (CoT) reasoning even for simple inputs and incurring high inference cost. However, naively shortening reasoning length can degrade reasoning accuracy, as concise reasoning may be insufficient for certain inputs and lacks explicit supervision. We propose Auto Long-Short Reasoning (AutoL2S), a distillation framework that empowers non-reasoning LLMs to think thoroughly but only when necessary. AutoL2S first learns a lightweight switching token with verified long-short CoTs to enable instance-wise long-short reasoning selection. Then it leverages long-short reasoning rollouts induced by switching tokens within a GRPO-style loss to improve reasoning efficiency while maintaining accuracy. Experiments demonstrate that AutoL2S effectively reduces reasoning length up to 71% with minimal accuracy loss, yielding markedly better trade-off in token length and inference time while preserving accuracy. The code is available at https://github.com/amandaluof/AutoL2S.

Large Language Models (LLMs) are increasingly adopted across real-world applications, yet traditional evaluations rely on expensive, domain-specific ground-truth labels that are often unavailable or infeasible. We introduce a ground-truth-free evaluation framework focused on reasoning consistency and instruction following, shifting the emphasis from correctness—which is elusive without labels—to transparent, coherent, evidence-based reasoning. Each model response must include a direct answer, a structured multi-step explanation, and supporting evidence, all assessed via semantic similarity and output adherence checks. We further propose TopK-ReRank, which refines rankings by constructing a consensus answer from the most reliable models, reducing ambiguity across diverse reasoning styles. Experiments show that our framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches, providing a more interpretable and efficient alternative for evaluating LLMs in the absence of ground-truth labels.

Large Reasoning Models (LRMs) are often bottlenecked by the high cost of output tokens. We show that a significant portion of these tokens are useless self-repetitions — what we call “word salad” — that exhaust the decoding budget without adding value. Interestingly, we observe that LRMs are self-aware when trapped in these loops: the hidden states of ‘‘ tokens trailing each reasoning chunk exhibit patterns that allow us to detect word salad behavior on-the-fly via a single linear classifier. Once detected, a simple chop appended by a straightforward regeneration prompt yields substantial length savings with minimal quality loss. Our work offers WordSaladChopper (WSC) — a lightweight, turnkey component for LRM that is minimally invasive to its reasoning trajectory. Given its low overhead, strong savings, and the lack of semantic value of word salad tokens, we believe it is not too far-fetched to argue that WSC — or a similar component — is a must-have for all LRM applications with user experience in mind.