Large reasoning models (LRMs) achieve state-of-the-art performance on challenging benchmarks by generating long chains of intermediate steps, but their inference cost is dominated by decoding, where each new token must attend to the entire growing sequence. One approach to reduce this latency is to evict entries from the key-value (KV) cache, thereby reducing the active context used in attention computation. However, such sparse attention methods suffer from severe accuracy degradation on reasoning tasks due to cumulative selection errors and the evolving importance of tokens over long derivations. We present \textbf{DELTA}, a training-free sparse attention mechanism that improves computational efficiency without sacrificing model accuracy. DELTA partitions transformer layers into three groups: initial layers that use full attention, a small set of \emph{$Δ$-layers} that identify salient tokens via aggregated head-level attention scores, and subsequent sparse-attention layers that attend only to the selected subset. This design preserves the full KV cache in GPU memory for accuracy, while avoiding expensive full-attention computation over many layers. On reasoning benchmarks such as AIME and GPQA-Diamond, DELTA matches or surpasses full attention in accuracy, while reducing the number of attended tokens by up to $4.25\times$ and delivering $1.54\times$ end-to-end speedup. Our results show that selective reuse of intermediate attention maps offers a robust path toward efficient long-context reasoning. The code is available at https://github.com/hoenza/DELTA.
School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Institute for Advanced Algorithms Research, Shanghai（上海高级算法研究院）
Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning
Tsinghua University（清华大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Peking University（北京大学）；Qwen Large Model Application Team, Alibaba（阿里云文大模型应用团队）；Microsoft（微软）；Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院 ubiquitous 数据赋能重点实验室）
DELTA: Deliberative Multi-Agent Reasoning with Reinforcement Learning for Multimodal Psychological Counseling
Anhui University（安徽大学）；Hefei University of Technology（合肥工业大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合国家科学中心人工智能研究所）
Reflection Steering: Disentangling Reflection from Reasoning in Activation Space for Token-Efficient Inference
School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）；Centre for Learning, Teaching and Technology, The Education University of Hong Kong（香港教育大学学习、教学与技术中心）
GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning
School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）；Institute of Natural Sciences, Shanghai Jiao Tong University（上海交通大学自然科学研究院）
On the Token Value Inequality in Efficient Reasoning
Purdue University（普渡大学）；Rochester Institute of Technology（罗切斯特理工学院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；University of Missouri-Kansas City（密苏里大学堪萨斯城分校）；Rutgers University（罗格斯大学）；Meta AI
PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning
Peking University（北京大学）；Peking University Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）
