Thought calibration: Efficient and confident test-time scaling

• URL: http://arxiv.org/abs/2505.18404v1
• Date: Fri, 23 May 2025 22:17:18 GMT
• Title: Thought calibration: Efficient and confident test-time scaling
• Authors: Menghua Wu, Cai Zhou, Stephen Bates, Tommi Jaakkola,
• Abstract summary: Reasoning large language models achieve impressive test-time scaling by thinking for longer, but this performance gain comes at significant compute cost.<n>We propose thought calibration to decide dynamically when thinking can be terminated.<n>We realize this framework through lightweight probes that operate on top of the language model's hidden representations.
• Score: 11.028893528095196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning large language models achieve impressive test-time scaling by thinking for longer, but this performance gain comes at significant compute cost. Directly limiting test-time budget hurts overall performance, but not all problems are equally difficult. We propose thought calibration to decide dynamically when thinking can be terminated. To calibrate our decision rule, we view a language model's growing body of thoughts as a nested sequence of reasoning trees, where the goal is to identify the point at which novel reasoning plateaus. We realize this framework through lightweight probes that operate on top of the language model's hidden representations, which are informative of both the reasoning structure and overall consistency of response. Based on three reasoning language models and four datasets, thought calibration preserves model performance with up to a 60% reduction in thinking tokens on in-distribution data, and up to 20% in out-of-distribution data.

Related papers

• Inverse Scaling in Test-Time Compute [51.16323216811257]
  Extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance.<n>We identify five distinct failure modes when models reason for longer.<n>These findings suggest that while test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns.
  arXiv  Detail & Related papers  (2025-07-19T00:06:13Z)
• Accelerated Test-Time Scaling with Model-Free Speculative Sampling [58.69141724095398]
  We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach.<n>We show that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding.<n>As a model-free approach, STAND can be applied to any existing language model without additional training.
  arXiv  Detail & Related papers  (2025-06-05T07:31:18Z)
• CoThink: Token-Efficient Reasoning via Instruct Models Guiding Reasoning Models [56.40065909544213]
  Large language models (LLMs) benefit from increased test-time compute, a phenomenon known as test-time scaling.<n>However, reasoning-optimized models often overthink even simple problems, producing excessively verbose outputs and leading to low token efficiency.<n>We identify two key causes of this verbosity: (1) reinforcement learning reduces the information density of forward reasoning, and (2) backward chain-of thought training encourages redundant and often unnecessary verification steps.
  arXiv  Detail & Related papers  (2025-05-28T06:24:45Z)
• Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models [42.407188124841234]
  Landscape of thoughts is a tool to inspect the reasoning paths of chain-of-thought on any multi-choice dataset.<n>It distinguishes between strong and weak models, correct and incorrect answers, as well as different reasoning tasks.<n>It also uncovers undesirable reasoning patterns, such as low consistency and high uncertainty.
  arXiv  Detail & Related papers  (2025-03-28T06:09:51Z)
• Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching [60.04718679054704]
  Chain-of-Thought prompting elicits step-by-step problem solving, but often at the cost of excessive verbosity in intermediate outputs.<n>We propose Sketch-of-Thought (SoT), a prompting framework that integrates cognitively inspired reasoning paradigms with linguistic constraints.<n>SoT achieves token reductions of up to 78% with minimal accuracy loss across 15 reasoning datasets.
  arXiv  Detail & Related papers  (2025-03-07T06:57:17Z)
• LINGOLY-TOO: Disentangling Memorisation from Knowledge with Linguistic Templatisation and Orthographic Obfuscation [1.2576388595811496]
  We introduce LINGOLY-TOO, a challenging reasoning benchmark grounded in natural language.<n>We permute reasoning problems written in real languages to generate numerous question variations.<n>Experiments and analyses show that models can circumvent reasoning and answer from prior knowledge.
  arXiv  Detail & Related papers  (2025-03-04T19:57:47Z)
• Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach [70.44265766483633]
  We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space.<n>Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time.<n>We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically.
  arXiv  Detail & Related papers  (2025-02-07T18:55:02Z)
• Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision [120.40788744292739]
  We propose a two-player paradigm that separates the roles of reasoning and critique models. We first propose AutoMathCritique, an automated and scalable framework for collecting critique data. We demonstrate that the critique models consistently improve the actor's performance on difficult queries at test-time.
  arXiv  Detail & Related papers  (2024-11-25T17:11:54Z)
• On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks [17.329365493094542]
  We present a principled empirical study of the performance of GPT-4 in three domains: Game of 24, Graph Coloring, and STRIPS planning. We observe significant performance collapse with self-critique and significant performance gains with sound external verification.
  arXiv  Detail & Related papers  (2024-02-12T23:11:01Z)
• Confident Adaptive Language Modeling [95.45272377648773]
  CALM is a framework for dynamically allocating different amounts of compute per input and generation timestep. We demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $times 3$ -- while provably maintaining high performance.
  arXiv  Detail & Related papers  (2022-07-14T17:00:19Z)
• Thought Flow Nets: From Single Predictions to Trains of Model Thought [39.619001911390804]
  When humans solve complex problems, they rarely come up with a decision right-away. Instead, they start with an intuitive decision reflecting upon it, spot mistakes, resolve contradictions and jump between different hypotheses.
  arXiv  Detail & Related papers  (2021-07-26T13:56:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.