Long Is More Important Than Difficult for Training Reasoning Models
Fuente:
arXiv
Saved in:
| Main Authors: | Shen, Si, Huang, Fei, Zhao, Zhixiao, Liu, Chang, Zheng, Tiansheng, Zhu, Danhao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
by: Shen, Si, et al.
Published: (2024)
by: Shen, Si, et al.
Published: (2024)
Attention Reveals More Than Tokens: Training-Free Long-Context Reasoning with Attention-guided Retrieval
by: Zhang, Yuwei, et al.
Published: (2025)
by: Zhang, Yuwei, et al.
Published: (2025)
MoreHopQA: More Than Multi-hop Reasoning
by: Schnitzler, Julian, et al.
Published: (2024)
by: Schnitzler, Julian, et al.
Published: (2024)
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps
by: Yu, Yijiong, et al.
Published: (2024)
by: Yu, Yijiong, et al.
Published: (2024)
Training With "Paraphrasing the Original Text" Teaches LLM to Better Retrieve in Long-context Tasks
by: Yu, Yijiong, et al.
Published: (2023)
by: Yu, Yijiong, et al.
Published: (2023)
Confidence Should Be Calibrated More Than One Turn Deep
by: Zhang, Zhaohan, et al.
Published: (2026)
by: Zhang, Zhaohan, et al.
Published: (2026)
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
by: Song, Xurui, et al.
Published: (2025)
by: Song, Xurui, et al.
Published: (2025)
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
by: Liu, Chengyuan, et al.
Published: (2024)
by: Liu, Chengyuan, et al.
Published: (2024)
Can reasoning models comprehend mathematical problems in Chinese ancient texts? An empirical study based on data from Suanjing Shishu
by: Liu, Chang, et al.
Published: (2025)
by: Liu, Chang, et al.
Published: (2025)
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage
by: Liu, Ruilin, et al.
Published: (2025)
by: Liu, Ruilin, et al.
Published: (2025)
CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
by: Shen, Junzhe, et al.
Published: (2026)
by: Shen, Junzhe, et al.
Published: (2026)
Training-Free Long-Context Scaling of Large Language Models
by: An, Chenxin, et al.
Published: (2024)
by: An, Chenxin, et al.
Published: (2024)
QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management
by: Shen, Weizhou, et al.
Published: (2025)
by: Shen, Weizhou, et al.
Published: (2025)
When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
by: McGovern, Hope, et al.
Published: (2026)
by: McGovern, Hope, et al.
Published: (2026)
PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
by: Hu, Sihao, et al.
Published: (2024)
by: Hu, Sihao, et al.
Published: (2024)
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
by: Huang, Tiansheng, et al.
Published: (2024)
by: Huang, Tiansheng, et al.
Published: (2024)
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering
by: Long, Yitao, et al.
Published: (2025)
by: Long, Yitao, et al.
Published: (2025)
From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale
by: Mahapatra, Rohan
Published: (2026)
by: Mahapatra, Rohan
Published: (2026)
Question Translation Training for Better Multilingual Reasoning
by: Zhu, Wenhao, et al.
Published: (2024)
by: Zhu, Wenhao, et al.
Published: (2024)
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
by: Wan, Fanqi, et al.
Published: (2025)
by: Wan, Fanqi, et al.
Published: (2025)
Hidden in the Haystack: Smaller Needles are More Difficult for LLMs to Find
by: Bianchi, Owen, et al.
Published: (2025)
by: Bianchi, Owen, et al.
Published: (2025)
Trivial Vocabulary Bans Improve LLM Reasoning More Than Deep Linguistic Constraints
by: Jehu-Appiah, Rodney
Published: (2026)
by: Jehu-Appiah, Rodney
Published: (2026)
Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training
by: Liu, Junxiao, et al.
Published: (2026)
by: Liu, Junxiao, et al.
Published: (2026)
Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More
by: Wen, Zichen, et al.
Published: (2025)
by: Wen, Zichen, et al.
Published: (2025)
LLM-Metrics: Measuring Research Impact Through Large Language Model Memory
by: Shen, Si, et al.
Published: (2026)
by: Shen, Si, et al.
Published: (2026)
Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons
by: Zhou, Shijia, et al.
Published: (2024)
by: Zhou, Shijia, et al.
Published: (2024)
Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?
by: Chung, Jiwan, et al.
Published: (2025)
by: Chung, Jiwan, et al.
Published: (2025)
Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting
by: Shen, Si, et al.
Published: (2025)
by: Shen, Si, et al.
Published: (2025)
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
by: Men, Xin, et al.
Published: (2024)
by: Men, Xin, et al.
Published: (2024)
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?
by: Aggarwal, Piush, et al.
Published: (2024)
by: Aggarwal, Piush, et al.
Published: (2024)
Should LLM Safety Be More Than Refusing Harmful Instructions?
by: Maskey, Utsav, et al.
Published: (2025)
by: Maskey, Utsav, et al.
Published: (2025)
R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search
by: Wang, Yibo, et al.
Published: (2025)
by: Wang, Yibo, et al.
Published: (2025)
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs
by: Chen, Jierun, et al.
Published: (2025)
by: Chen, Jierun, et al.
Published: (2025)
The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
by: Zhu, Wenhao, et al.
Published: (2024)
by: Zhu, Wenhao, et al.
Published: (2024)
LIMO: Less is More for Reasoning
by: Ye, Yixin, et al.
Published: (2025)
by: Ye, Yixin, et al.
Published: (2025)
T3DM: Test-Time Training-Guided Distribution Shift Modelling for Temporal Knowledge Graph Reasoning
by: Si, Yuehang, et al.
Published: (2025)
by: Si, Yuehang, et al.
Published: (2025)
Joint Enhancement of Relational Reasoning for Long-Context LLMs
by: Chen, Zhirui, et al.
Published: (2025)
by: Chen, Zhirui, et al.
Published: (2025)
Generating Difficult-to-Translate Texts
by: Zouhar, Vilém, et al.
Published: (2025)
by: Zouhar, Vilém, et al.
Published: (2025)
More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning
by: Zhao, Yike, et al.
Published: (2025)
by: Zhao, Yike, et al.
Published: (2025)
Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
by: Yu, Qinan, et al.
Published: (2026)
by: Yu, Qinan, et al.
Published: (2026)
Similar Items
-
RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
by: Shen, Si, et al.
Published: (2024) -
Attention Reveals More Than Tokens: Training-Free Long-Context Reasoning with Attention-guided Retrieval
by: Zhang, Yuwei, et al.
Published: (2025) -
MoreHopQA: More Than Multi-hop Reasoning
by: Schnitzler, Julian, et al.
Published: (2024) -
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps
by: Yu, Yijiong, et al.
Published: (2024) -
Training With "Paraphrasing the Original Text" Teaches LLM to Better Retrieve in Long-context Tasks
by: Yu, Yijiong, et al.
Published: (2023)