Adaptive Reasoning in Language Models

Latest papers

13 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
Oct 13, 2026Taehyeon Kim, Hyunsoo Lee, Youngsoo Jang +1Adaptive Reasoning in Language Models Efficient Language Model Reasoning
LG AI Research · UNIST · University of Illinois Chicago
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation
Oct 8, 2026Yiruo Cheng, Shen Huang, Xiaoshuai Song +5Adaptive Reasoning in Language Models Agentic Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China · Alibaba Token Hub, Alibaba Group
MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller
Sep 29, 2026Zhibin Wen, Tao Han, Lei Bai +2Adaptive Reasoning in Language Models RL for Language Model Reasoning
Southern University of Science and Technology · The Hong Kong University of Science and Technology · Shanghai Artificial Intelligence Laboratory +1
Hunyuan-A13B Technical Report
Sep 23, 2026Tencent Hunyuan Team, Ao Liu, Botong Zhou +72Adaptive Reasoning in Language Models Efficient Language Model Reasoning
Selective State-Space Adaptation and Retrieval for Language Model Reasoning
Jul 21, 2026Atahan Dokme, Larry HeckRetrieval-Augmented Generation Selective SSMs
AI Virtual Assistant (AVA) Lab Georgia Institute of Technology
Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning
Jul 9, 2026Yiyang Fang, Pei Fu, Jinjie Li +7Adaptive Reasoning in Language Models Efficient Multimodal Inference
Wuhan University · The Hong Kong University of Science and Technology · Xiaomi Inc +2
SuCo: Sufficiency-guided Continuous Adaptive Reasoning
Jun 16, 2026Jiahao Wang, Bingyu Liang, Chenhao Hu +5Adaptive Reasoning in Language Models RL for Language Models
Harbin Institute of Technology (Shenzhen), Guangdong, China. · TeleAI of China Telecom, China.
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
Jun 15, 2026Hanyu Lin, Min Cai, Jiawei Wen +1Latent Reasoning LLM Reasoning
1Shenzhen University · University of Alberta
AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering
May 29, 2026Yuxin Wang, Jiahao Lu, Qifeng Wu +5Multi-Hop QA Adaptive Reasoning in Language Models
1Computer Science, Fudan University · Institute of Modern Languages and Linguistics, Fudan University · 3Shanghai Innovation Institute +1
x1: Learning to Think Adaptively Across Languages and Cultures
Apr 18, 2026Yangfan Ye, Xiaocheng Feng, Xiachong Feng +8Multilingual Language Models Cross-Lingual Reasoning in Language Models
Harbin Institute of Technology · Peng Cheng Laboratory · The University of Hong Kong +1
HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation
Apr 9, 2026He Zhao, Yijun Yang, Zichuan Lin +2Adaptive Reasoning in Language Models Embodied Navigation
Nanyang Technological University · Tencent · Independent Researcher
CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning
Mar 9, 2026Siye Wu, Jian Xie, Yikai Zhang +1Adaptive Reasoning in Language Models Adaptive Computation
♠Fudan University
Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
Aug 17, 2025Xinda Jia, Jinpeng Li, Zezhong Wang +6Adaptive Reasoning in Language Models LLM Tool Use
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Huawei Technologies Co., Ltd., Beijing 100084, China · Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong 999077, China +3

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Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking

Taehyeon Kim, Hyunsoo Lee, Youngsoo Jang, Moontae Lee

LG AI Research · UNIST · University of Illinois Chicago

Adaptive Reasoning in Language Models Efficient Language Model Reasoning Large Reasoning Models Overthinking in Language Models RL for Language Model Reasoning

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Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocates uniformly heavy computation to queries of varying difficulty. While proprietary models mitigate this via opaque routing, open-source LRMs still lack an efficient mechanism to internalize adaptive reasoning due to both expensive training cost and limited disclosure of training recipes. In response, we introduce RPO (Root-token Policy Optimization), a framework that enables LRMs to self-determine when to reason by training only the initial root token (e.g., whether to invoke the <think> tag) via group relative reward and group-wise advantages. By focusing on this pivotal branching point, RPO drastically reduces training overhead and VRAM usage. Across multiple model families and scales, RPO learns difficulty-aware adaptive thinking at just ~2% of the training compute of prior adaptive-reasoning methods.