SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought

• URL: http://arxiv.org/abs/2508.00574v1
• Date: Fri, 01 Aug 2025 12:17:35 GMT
• Title: SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought
• Authors: Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng,
• Abstract summary: Chain-of-Thought (CoT) reasoning incurs significant time costs due to the generation of discrete CoT tokens (DCoT)<n>Existing Continuous CoT methods are hampered by indirect fine-tuning, limited alignment, or inconsistent targets.<n>We propose textitSynAdapt, an innovative efficient reasoning framework.
• Score: 8.287063165175667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Chain-of-Thought (CoT) reasoning improves model performance, it incurs significant time costs due to the generation of discrete CoT tokens (DCoT). Continuous CoT (CCoT) offers a more efficient alternative, but existing CCoT methods are hampered by indirect fine-tuning, limited alignment, or inconsistent targets. To overcome these limitations, we propose \textit{SynAdapt}, an innovative efficient reasoning framework. Specifically, \textit{SynAdapt} generates the synthetic CCoT to serve as a precise and effective alignment target for LLMs. This synthetic CCoT explicitly guides the LLM to learn CCoT and derive accurate answers directly. Furthermore, relying solely on CCoT is insufficient for solving hard questions. To address this, \textit{SynAdapt} integrates a difficulty classifier that leverages both question context and CCoT to identify hard questions. CCoT can effectively help identify hard questions after some brief reasoning. We then adaptively prompt the LLM to re-think these hard questions for improved performance. Extensive experimental results across various benchmarks from different difficulty levels strongly demonstrate the effectiveness of our method, achieving the best accuracy-efficiency trade-off.

Related papers

• Compressing Chain-of-Thought in LLMs via Step Entropy [12.576398947428988]
  Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency.<n>We introduce a novel CoT compression framework based on step entropy, a metric that quantifies the informational contribution of individual reasoning steps to identify redundancy.
  arXiv  Detail & Related papers  (2025-08-05T11:48:18Z)
• ECCoT: A Framework for Enhancing Effective Cognition via Chain of Thought in Large Language Model [1.0760366210656895]
  ECCoT is a framework to evaluate and refine reasoning chains in Large Language Models.<n>It improves interpretability, reduces biases, and enhances the trustworthiness of LLM-based decision-making.
  arXiv  Detail & Related papers  (2025-06-24T13:09:53Z)
• TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs [50.820065021136024]
  DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs)<n>Recent methods have attempted to replicate R1's reasoning capabilities in multimodal settings.<n>We propose TACO, a novel reinforcement learning algorithm for visual reasoning.
  arXiv  Detail & Related papers  (2025-05-27T06:30:48Z)
• Efficient Long CoT Reasoning in Small Language Models [26.579760423359673]
  It is challenging to directly train small language models (SLMs) to emerge long chain-of-thought (CoT) reasoning steps.<n>We propose a simple-yet-effective method to prune unnecessary steps in long CoT, and then employ an on-policy method for the SLM itself to curate valid and useful long CoT training data.
  arXiv  Detail & Related papers  (2025-05-24T00:22:52Z)
• Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning [81.50681925980135]
  We propose Stepwise Reasoning Checkpoint Analysis (SRCA), a framework that introduces checkpoints between reasoning steps.<n>It incorporates two key strategies: (1) Answer-Clustered Search, which groups reasoning paths by their intermediate checkpoint answers to maintain diversity while ensuring quality, and (2) Checkpoint Candidate Augmentation, which leverages all intermediate answers for final decision-making.<n>Our approach effectively reduces path homogenization and creates a fault-tolerant mechanism by utilizing high-quality intermediate results.
  arXiv  Detail & Related papers  (2025-05-23T12:42:50Z)
• AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning [30.265984245328124]
  Chain-of-Thought prompting indiscriminately generates lengthy reasoning steps for all queries.<n>AdaCoT (Adaptive Chain-of-Thought) is a novel framework enabling LLMs to adaptively decide when to invoke CoT.<n>A key technical contribution is Selective Loss Masking (SLM), designed to counteract decision boundary collapse.
  arXiv  Detail & Related papers  (2025-05-17T08:27:00Z)
• Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs [45.33160999781074]
  Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs)<n>We introduce UnCert-CoT, an approach designed to enhance code generation by incorporating an uncertainty-aware CoT reasoning mechanism.
  arXiv  Detail & Related papers  (2025-03-19T15:40:45Z)
• 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)
• ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting [124.69672273754144]
  Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs) Existing CoT approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. We introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts.
  arXiv  Detail & Related papers  (2024-03-21T11:34:26Z)
• Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models [81.01397924280612]
  Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains.
  arXiv  Detail & Related papers  (2023-04-23T13:54:39Z)
• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters [82.84696222087396]
  Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs) We show that CoT reasoning is possible even with invalid demonstrations.
  arXiv  Detail & Related papers  (2022-12-20T05:20:54Z)

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.