Advancements in Efficient Reasoning for Large Language Models

The field of large language models is moving towards improving the efficiency and effectiveness of reasoning capabilities. Recent research has focused on developing methods to reduce redundant reasoning steps, compress chain-of-thought (CoT) reasoning, and optimize the use of computational resources. Notable approaches include using adaptive length penalties, bidirectional compression, and dynamic switching between long and short CoT strategies. These methods aim to balance reasoning accuracy and computational efficiency, offering practical benefits for real-world applications. Some papers have also investigated the effectiveness of CoT reasoning, suggesting that it may not always elicit genuine, abstract reasoning, but rather imitate the form of reasoning through structural constraints. In the section below, we highlight a few papers that are particularly noteworthy for their innovative approaches to advancing the field.

Noteworthy Papers

SCOUT: introduces a lightweight fine-tuning framework that enables Flow CoT style reasoning without the need for pretraining, achieving up to 1.8% gains under fine-tuning.
A*-Thought: proposes an efficient tree search-based framework that balances performance and efficiency, improving the performance of QwQ-32B by 2.39× with low-budget.

Sources

SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought

Reasoning Can Hurt the Inductive Abilities of Large Language Models

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion

A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings

AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation

Answer Convergence as a Signal for Early Stopping in Reasoning

TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression

CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

Reason from Future: Reverse Thought Chain Enhances LLM Reasoning

Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models

EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation

Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models

Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation

Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning