Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 15 references
TL;DR
This work proposes Step-wise Training for In-context Reasoning (STIR), a model to dynamically decide when to retrieve a single logically consistent next step, just using the current problem and its intermediate state as the query.
Abstract
Large language models (LLMs) have shown strong performance in mathematical reasoning, supported by approaches such as In-Context Learning (ICL) and Retrieval-Augmented Generation (RAG). However, existing methods often provide problem-level examples, which is too coarse-grained for multi-step reasoning to cause informational redundancy, and structural misalignment. To address this limitation, we propose Step-wise Training for In-context Reasoning (STIR) to provide step-synchronized and logically targeted guidance to enhance the model's mathematical reasoning capabilities. STIR enables a model to dynamically decide when to retrieve a single logically consistent next step, just using the current problem and its intermediate state as the query. First, We decompose expert solutions into Step-Level Reasoning Units inspired by human thinking patterns. Leveraging this data, a Step Retriever is trained for logical continuity to map current reasoning states to relevant subsequent steps. Then a Step Reasoner is trained to decide when to retrieve tailored step examples and incorporates this guidance into reasoning. We further extend STIR with a Process-aware Reinforcement Learning phase using Group Relative Policy Optimization to learn to self-formulate search queries and optimizes the decision-making policy. Experiments on seven benchmarks demonstrate that STIR achieves accuracy improvements ranging from 1.86% to 17.96%, maintaining lower token efficiency than baselines. Analysis via our proposed DSM, TCN and RCR metrics shows that STIR improves reasoning capability, achieving DSM scores ranging from 4.54 to 29.87 across backbones and significant improvements over the baseline in both TCN and RCR.
DynaRule is proposed, an end-to-end framework that injects the given rules into the KV cache and turns retrieval into an internal, learnable, step-wise process, and can re-attend to the most relevant rules at each step, dynamically replacing outdated ones to support more stable multi-step reasoning.
Bohan Yu, Pengfei Cao, Chen Han et al.· 1 citation
Experimental results show that PD-CoTD significantly enhances the mathematical reasoning abilities of SLMs and provides an effective solution for deploying powerful reasoning in resource-constrained environments.
Zhe Wang, Long Zhang, Fengbo Zheng et al.· International journal of sof...· 0 citations
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
Jiaoyang Li, Junhao Ruan, Shengwei Tang et al.· 0 citations
Large language model agents increasingly rely on reusable skills to extend their capabilities beyond parametric knowl- edge. However, retrieving the appropriate skill from a large- scale library remains challenging because realistic user re- quests are often concise and underspecified, stating only the task goal while leaving the required capabilities and execu- tion steps implicit. Existing benchmarks provide limited cov- erage of such requests. To address this gap, we introduce SkillReason-Bench, a large-scale cross-domain benchmark containing 3,729 queries and a retrieval corpus of 61,228 skills spanning nine domains. We further propose SkillRea- son, a two-stage framework that uses chain-of-thought rea- soning as training-time supervision for skill retrieval. In Stage I, capability reasoning traces generated by a stronger teacher provide explicit supervision through contrastive learning, re- trieval distribution alignment, and language modeling, en- couraging the retriever to internalize capability reasoning in its query representation. In Stage II, a retrieval-guided GRPO objective encourages the model to explore reasoning trajecto- ries better suited to its own capabilities and more effective for retrieval. At inference, SkillReason directly encodes the orig- inal query without autoregressive CoT generation, preserv- ing efficient query-only retrieval. Extensive experiments on SkillReason-Bench, SkillRet, and SRA-Bench show that Skill- Reason achieves state-of-the-art performance across all three benchmarks, demonstrating that reasoning-enhanced training better bridges the semantic gap between high-level task goals and skill capabilities.
Donghong Jiang, Endian Lin, Luoping Cui et al.· 0 citations
Large Language Models (LLMs) demonstrate significant potential in sequential recommendation, and leveraging their Chain-of-Thought (CoT) reasoning capabilities can further unlock profound user preference understanding. However, deploying explicit CoT reasoning in real-world systems faces prohibitive challenges: (i) the conflict between the large model scale required for high-fidelity reasoning and the resource constraints of online services, and (ii) the excessive latency introduced by auto-regressive rationale generation. To address these issues, we propose I Reasoning via Multi-Teacher Distillation (IRMD), a novel framework that 'compiles' the reasoning abilities of large teacher LLMs into a lightweight student Small Language Model (SLM). IRMD first employs a Multi-Teacher CoT Synthesis with Dual-Constraint Rejection Sampling module to generate a high-quality, diverse set of reasoning paths. Subsequently, our Annealing-Scheduled Reasoning Distillation strategy progressively trains the student to internalize this logic, transitioning from mimicking explicit CoT to performing purely implicit reasoning. Extensive experiments on multiple benchmark datasets demonstrate that IRMD significantly outperforms state-of-the-art baselines in both recommendation accuracy and inference efficiency. Our code is accessible at https://github.com/Cxx-0/IRMD.
The O1 Embedder is proposed, a novel approach aiming to endow retrieval models with similar capabilities to address challenges like multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships.
Ruiran Yan, Wen Xiong, Ze Liu et al.· Annual Meeting of the Associ...· 2 citations
