Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions. A common approach to improving...
arXiv:2604.16778v3 Announce Type: replace-cross Abstract: Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learni...
arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.
arXiv:2610.19367v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended seq...
arXiv:2610.19439v1 Announce Type: new Abstract: While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substanti...
The study explores whether instruction-tuned large language models (LLMs) can analyze collaborative student sensemaking without task-specific training, and whether adding structured knowledge-state information enhances this analysis. Two mid-size LLMs were evaluated on 23 expert-labeled episodes under various prompting conditions, showing that reasoning-enabled prompts better detect unsuccessful sensemaking and that knowledge-state diagnostics improve agreement with experts. No single configuration outperformed others across all sensemaking dimensions, highlighting the task’s multidimensional nature.
arXiv:2604. 22770v2 Announce Type: replace-cross Abstract: Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency.
State-OPD introduces a state‑grounded on‑policy distillation framework that separates the objective task and action‑observation record from the student’s internal reasoning trajectory. By removing prior student reasoning and resampling complete reasoning‑action responses at high‑entropy states, the method improves teacher supervision and action diversity. Evaluations on ALFWorld, ScienceWorld, and WebShop show State‑OPD achieves the highest macro‑average success rates for Qwen3‑1.7B and Qwen3‑4B students, even surpassing a larger teacher model on certain tasks.
CoLearn is an interactive, agentic tutoring system that learns about each learner through a persistent memory of mastery and misconceptions, updated with a Bayesian Knowledge Tracing model that uses a large language model as an observation function. It generates personalized questions targeting the learner’s weakest topics and recurring misconceptions, and provides a live evidence view for progress visualization and blind A/B comparison. In blind A/B tests, learners preferred questions conditioned on this memory 68‑69% of the time, and simulations show the agent’s belief converges toward the learner’s true mastery.
The paper introduces masked self‑distillation, a post‑training framework that trains a language model to internalize portions of its own intermediate reasoning traces. By varying the fraction of trace internalized, the authors demonstrate that models can achieve higher inference efficiency and improved task performance on math and graph‑coloring problems. Experiments on Qwen3‑4B and Qwen3‑8B show that the method generalizes well to in‑domain out‑of‑distribution cases without catastrophic forgetting, and that supervised fine‑tuning alone can reduce trace length at the expense of generalization.
arXiv:2610.23234v1 Announce Type: new Abstract: In a hierarchical multi-agent system, a Leader assigns tasks and subagents carry them out. Jointly training the Leader and the subagents is difficult b...
SOD: Step-wise On-policy Distillation for Small Language Model Agents proposes a new framework that adaptively reweights distillation strength at each reasoning step based on step-level divergence. This approach mitigates cascading errors in tool-integrated reasoning by attenuating misleading teacher signals in high-divergence regions while preserving dense guidance where student and teacher align. Experiments on math, science, and code benchmarks show up to 20.86% improvement over the second-best baseline, with a 0.6B student scoring 26.13% on AIME 2025.