Multi-Turn Language Model Reasoning

Follow this topic Ask about this topic

Latest papers

15 papers6 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Learning What to Ask and What to Believe: Distilling a Variational Bayesian Agent into Language Models
Oct 31, 2026Baichuan Li, Yanling Chang, Alfredo GarciaLanguage Model Distillation Multi-Turn Language Model Reasoning
Southern Methodist University · University of Virginia
SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection
Oct 23, 2026Jiawei Dong, Zilong Bai, Pengtian zhu +1Hallucination Detection LLM Hallucination Detection
Patsnap Pte. Ltd., Suzhou, China
CollabSciReM: Aligning Language Models for Scientific Conversations via Science-Aware Reward Decomposition
Oct 20, 2026Sasi Kiran Gaddipati, Gollam Rabby, Sören AuerLLM Alignment Reward Modeling
TIB—Leibniz Information Centre for Science and Technology, Hannover, Germany · L3S Research Center, Hannover, Germany
Computation, Not Memory: Chain-of-Thought and State Tracking in LLM Agents
Oct 19, 2026Pedram Mouseli, Massieh MoayediCoT Reasoning Multi-Turn Language Model Reasoning
University of Toronto
SteerSearch: Interactive Search Reasoning with Mid-Response Steering
Oct 16, 2026Udit Thakur, Baohao Liao, Matteo Nulli +4Language Model Steering Retrieval-Augmented Reasoning
Language Technology Lab, University of Amsterdam · eBay
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving
Oct 12, 2026Xin Cheng, Junming Yang, Ning XuMulti-Turn Language Model Reasoning LLM Reasoning
School of Computer Science and Engineering, Southeast University, Nanjing, China.
Geometry of Divergence: Tracking Hidden-State Trajectories for Adaptive Multi-Turn Reasoning
Aug 31, 2026Jie Liang, Zhengxin Yu, Hamid Nasiri +1Multi-Turn Language Model Reasoning Long-Horizon LLM Agents
School of Computing and Communications, Lancaster University, UK · School of Computing and Engineering, University of Huddersfield, UK
Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference
Aug 4, 2026Shahrukh Mohiuddin, Chalamalasetti Kranti, Sherzod Hakimov +1LLM Evaluation Multi-Turn Language Model Reasoning
Computational Linguistics, Department of Linguistics University of Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
Jun 29, 2026Ankur Samanta, Akshayaa Magesh, Tal Lancewicki +7LLM Evaluation Multi-Turn Language Model Reasoning
Meta AI · Columbia University · Work done at Meta +2
When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models
Jun 9, 2026Sai Kartheek Reddy Kasu, Nils Lukas, Samuele PoppiCoT Faithfulness LLM Alignment
Independent Researcher · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models
May 28, 2026Zizhuo Lin, Quanling Liu, Jinsheng Quan +6LLM Grounding Multi-Turn Language Model Reasoning
Zhejiang University · University of Science and Technology of China
Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents
Apr 6, 2026Neharika Jali, Anupam Nayak, Gauri JoshiMulti-Turn Language Model Reasoning Efficient Language Model Reasoning
Carnegie Mellon University
Adaptive Stopping for Multi-Turn LLM Reasoning
Apr 1, 2026Xiaofan Zhou, Huy Nguyen, Bo Yu +2Multi-Turn Language Model Reasoning Efficient Language Model Reasoning
Department of Computer Science, University of Illinois Chicago, Chicago, IL 60607, USA · Data science, Augustana College, Rock Island, IL 61201, USA · Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA
Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction
Mar 5, 2026Xingwu Chen, Zhanqiu Zhang, Yiwen Guo +1Multi-Turn Language Model Reasoning RL for Language Model Reasoning
School of Computing and Data Science, The University of Hong Kong · LIGHTSPEED · Independent Researcher
In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
Oct 1, 2025Youngbin Choi, Minjong Lee, Saemi Moon +4LLM Reliability Multi-Turn Language Model Reasoning
Graduate School of Artificial Intelligence, POSTECH · Department of Computer Science and Engineering, POSTECH

Oct 31, 2026J/K move · Enter open · S save

Learning What to Ask and What to Believe: Distilling a Variational Bayesian Agent into Language Models

Baichuan Li, Yanling Chang, Alfredo Garcia

Southern Methodist University · University of Virginia

Language Model Distillation Multi-Turn Language Model Reasoning

Open paper AskPDF arXiv

Interactive tasks such as eliciting a user's preferences or diagnosing a hidden mechanism require an agent to track its uncertainty over a latent state, ask informative questions and act on the answers. Language models have no explicit mechanism for this, whereas Bayesian information seeking provides one through posterior inference and expected information gain (EIG), and we ask whether it can be distilled into an 8B language model. Our teacher, a lightweight Bayesian agent with a sequential variational belief and EIG query selection, matches or approaches exact and particle references on three task families. We generate synthetic interactions with the teacher on episodes disjoint from the benchmark and fine-tune \Student{} to reproduce its queries and its belief state as a structured scratchpad. On three Multi-Turn Puzzles tasks the student raises Circuit Decoding from 0.00 to 0.93 success, lowers the normalized rank of the recommended film from 0.50 to 0.10 and cuts word guessing from 32 to 3.4 attempts, exceeding the strongest reported language models on the two comparable tasks. Exposing the belief state closes much of the gap where the decision is an implicit computation rather than a read-out of the interaction; in matched teacher contrasts, EIG supervision consistently outperforms random querying, whereas greater inference fidelity does not uniformly improve the distilled student; a 70B language-model teacher does not match the Bayesian teacher in downstream student performance, and the learned skill does not transfer to an unseen task family. Variational Bayesian information seeking can thus be amortized into a language model by transferring the belief state that drives its questions and decisions.