Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
Not All Tokens Learn Alike: Attention Entropy Reveals Heterogeneous Signals in RL Reasoning
Gengyang Li, Zheng-Fan Wu, Siqi Bao +1
Reinforcement-learning-based post-training has become a key approach for improving the reasoning ability of large language models, but its token-level learning signals remain poorl…
cs.CL2026
SyncThink: A Training-Free Strategy to Align Inference Termination with Reasoning Saturation
Gengyang Li, Wang Cai, Yifeng Gao +1
Chain-of-Thought (CoT) prompting improves reasoning but often produces long and redundant traces that substantially increase inference cost. We present SyncThink, a training-free a…
cs.CL2025
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy
Gengyang Li, Yifeng Gao, Yuming Li +1
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…