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ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy
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ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy
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While Chain-of-Thought (CoT) prompting improves reasoning in large language models (LLMs), the excessive length of reasoning tokens increases latency and KV cache memory usage, and may even truncate final answers under context limits. We propose ThinkLess, an inference-efficient framework that terminates reasoning generation early and maintains output quality without modifying the model. Atttention analysis reveals that answer tokens focus minimally on earlier reasoning steps and primarily attend to the reasoning terminator token, due to information migration under causal masking. Building on this insight, ThinkLess inserts the terminator token at earlier positions to skip redundant reasoning while preserving the underlying knowledge transfer. To prevent format discruption casued by early termination, ThinkLess employs a lightweight post-regulation mechanism, relying on the model's natural instruction-following ability to produce well-structured answers. Without fine-tuning or auxiliary data, ThinkLess achieves comparable accuracy to full-length CoT decoding while greatly reducing decoding time and memory consumption.
Forward citations
Cited by 6 Pith papers
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Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
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CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning
CRISP compresses chain-of-thought by 50-60% using intrinsic attention saliency from the termination token to prune redundancy while preserving accuracy on math tasks.
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Asynchronous Reasoning: Training-Free Interactive Thinking LLMs
Using properties of positional embeddings, reasoning LLMs can be made to think, listen, and generate outputs asynchronously without any additional training, cutting time to first token to under 5 seconds.
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Are Large Reasoning Models Interruptible?
Interrupting large reasoning models or changing the problem mid-thought sharply degrades accuracy, with up to 60% drops and distinct failure modes: reasoning leakage, panic, and self-doubt.
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XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation
XRPO extends GRPO with adaptive rollout allocation, in-context example seeding for unsolved prompts, and novelty-weighted advantages, reporting roughly 1-4% higher accuracy and faster convergence.
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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.
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