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Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, reducing thinking tokens by 58.3% while maintaining near-identical performance.
A paper introduces an inference-time optimization for sparse MoE models by adjusting expert selection in late transformer layers, reducing reasoning tokens by 8.5% and latency by 10.9% without retraining, while maintaining accuracy.
Gemma-4-26B-a4B, with an updated chat template, outperforms Qwen3.6-MoE and Qwen3.5-MoE in fine-tuned instruct mode and reasoning efficiency.
Grug-35B-A3B is an open-source experimental model based on Qwen-3.6-35B-A3B that reduces reasoning token usage by approximately 69.8% while maintaining answer quality within 2% of the base model, resulting in faster generation and reduced context usage.
This paper introduces DyCon, a training-free framework that uses step-level embeddings to model evolving task difficulty and dynamically control reasoning depth in Large Reasoning Models, effectively reducing overthinking and improving efficiency without sacrificing accuracy.
ThoughtFold proposes a framework using introspective preference learning to reduce redundant explorations in Chain-of-Thought reasoning for Large Reasoning Models, achieving ~56% token reduction on DeepSeek-R1-Distill-Qwen-7B without accuracy loss.
This paper introduces the VPG-EA framework, which uses variational inference and posterior guidance to improve the reasoning efficiency of large language models by addressing the 'overthinking' phenomenon in chain-of-thought generation.
This paper introduces 'Hint Tuning,' a data-efficient method that reduces token usage in reasoning models by calibrating reasoning depth based on problem difficulty. It achieves significant token reduction (24–66%) on models like Qwen3-Thinking and DeepSeek-R1-Distill using only 1K self-annotated samples.