Saved in:
| Main Authors: | Li, Zheng, Song, Siyao, Ma, Jingyuan, Li, Rui, Zeng, Ying, Li, Minghao, Sui, Zhifang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21375 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?
by: Wang, Jingyuan, et al.
Published: (2025)
by: Wang, Jingyuan, et al.
Published: (2025)
HauntAttack: When Attack Follows Reasoning as a Shadow
by: Ma, Jingyuan, et al.
Published: (2025)
by: Ma, Jingyuan, et al.
Published: (2025)
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
by: Li, Zheng, et al.
Published: (2025)
by: Li, Zheng, et al.
Published: (2025)
ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
by: Li, Minghao, et al.
Published: (2025)
by: Li, Minghao, et al.
Published: (2025)
Self-Boosting Large Language Models with Synthetic Preference Data
by: Dong, Qingxiu, et al.
Published: (2024)
by: Dong, Qingxiu, et al.
Published: (2024)
EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance
by: Song, Siyao, et al.
Published: (2025)
by: Song, Siyao, et al.
Published: (2025)
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024)
by: Qin, Yiwei, et al.
Published: (2024)
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
by: Chu, Zheng, et al.
Published: (2023)
by: Chu, Zheng, et al.
Published: (2023)
LocateBench: Evaluating the Locating Ability of Vision Language Models
by: Chiang, Ting-Rui, et al.
Published: (2024)
by: Chiang, Ting-Rui, et al.
Published: (2024)
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal
by: Xie, Tinghao, et al.
Published: (2024)
by: Xie, Tinghao, et al.
Published: (2024)
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
by: Zhang, Junkai, et al.
Published: (2025)
by: Zhang, Junkai, et al.
Published: (2025)
Teaching Large Reasoning Models Effective Reflection
by: Wang, Hanbin, et al.
Published: (2026)
by: Wang, Hanbin, et al.
Published: (2026)
SambaLingo: Teaching Large Language Models New Languages
by: Csaki, Zoltan, et al.
Published: (2024)
by: Csaki, Zoltan, et al.
Published: (2024)
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models
by: Li, Lingyu, et al.
Published: (2024)
by: Li, Lingyu, et al.
Published: (2024)
TIDE-Bench: Task-Aware and Diagnostic Evaluation of Tool-Integrated Reasoning
by: Li, Yize, et al.
Published: (2026)
by: Li, Yize, et al.
Published: (2026)
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
by: Parmar, Mihir, et al.
Published: (2024)
by: Parmar, Mihir, et al.
Published: (2024)
Plug-and-Play Training Framework for Preference Optimization
by: Ma, Jingyuan, et al.
Published: (2024)
by: Ma, Jingyuan, et al.
Published: (2024)
Teaching Language Models to Reason with Tools
by: Li, Chengpeng, et al.
Published: (2025)
by: Li, Chengpeng, et al.
Published: (2025)
PsychCounsel-Bench: Evaluating the Psychology Intelligence of Large Language Models
by: Zeng, Min
Published: (2025)
by: Zeng, Min
Published: (2025)
Chip-Tuning: Classify Before Language Models Say
by: Zhu, Fangwei, et al.
Published: (2024)
by: Zhu, Fangwei, et al.
Published: (2024)
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
by: Ying, Yuchen, et al.
Published: (2026)
by: Ying, Yuchen, et al.
Published: (2026)
RNR: Teaching Large Language Models to Follow Roles and Rules
by: Wang, Kuan, et al.
Published: (2024)
by: Wang, Kuan, et al.
Published: (2024)
Do Large Language Models Mentalize When They Teach?
by: Harootonian, Sevan K., et al.
Published: (2026)
by: Harootonian, Sevan K., et al.
Published: (2026)
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
TasTe: Teaching Large Language Models to Translate through Self-Reflection
by: Wang, Yutong, et al.
Published: (2024)
by: Wang, Yutong, et al.
Published: (2024)
A Survey on In-context Learning
by: Dong, Qingxiu, et al.
Published: (2022)
by: Dong, Qingxiu, et al.
Published: (2022)
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
by: Wang, Xiaoxuan, et al.
Published: (2023)
by: Wang, Xiaoxuan, et al.
Published: (2023)
A Survey on Fairness in Large Language Models
by: Li, Yingji, et al.
Published: (2023)
by: Li, Yingji, et al.
Published: (2023)
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
by: Yang, Shuo, et al.
Published: (2025)
by: Yang, Shuo, et al.
Published: (2025)
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
by: LI, Yizhi, et al.
Published: (2024)
by: LI, Yizhi, et al.
Published: (2024)
LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners
by: Zheng, Junhao, et al.
Published: (2025)
by: Zheng, Junhao, et al.
Published: (2025)
GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
by: Costarelli, Anthony, et al.
Published: (2024)
by: Costarelli, Anthony, et al.
Published: (2024)
Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
by: Chen, Zixin, et al.
Published: (2026)
by: Chen, Zixin, et al.
Published: (2026)
PetroBench: A Benchmark for Large Language Models in Petroleum Engineering
by: Wang, Xiang, et al.
Published: (2026)
by: Wang, Xiang, et al.
Published: (2026)
Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
by: Yang, Xinming, et al.
Published: (2025)
by: Yang, Xinming, et al.
Published: (2025)
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
by: Li, Wei, et al.
Published: (2024)
by: Li, Wei, et al.
Published: (2024)
CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment
by: Feng, Rui, et al.
Published: (2025)
by: Feng, Rui, et al.
Published: (2025)
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
by: Wang, Weida, et al.
Published: (2025)
by: Wang, Weida, et al.
Published: (2025)
AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
by: Guo, Zhengkang, et al.
Published: (2026)
by: Guo, Zhengkang, et al.
Published: (2026)
SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
by: Xu, Jingxuan, et al.
Published: (2025)
by: Xu, Jingxuan, et al.
Published: (2025)
Similar Items
-
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?
by: Wang, Jingyuan, et al.
Published: (2025) -
HauntAttack: When Attack Follows Reasoning as a Shadow
by: Ma, Jingyuan, et al.
Published: (2025) -
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
by: Li, Zheng, et al.
Published: (2025) -
ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
by: Li, Minghao, et al.
Published: (2025) -
Self-Boosting Large Language Models with Synthetic Preference Data
by: Dong, Qingxiu, et al.
Published: (2024)