6 citations · 20 across the 47 of their papers we have counts for
19 papers · 1 filter
Scaling and Transferability of Annealing Strategies in Large Language Model Training
Siqi Wang, Zhengyu Chen, Teng Xiao +5
Learning rate scheduling is crucial for training large language models, yet understanding the optimal annealing strategies across different model configurations remains challenging…
A Preliminary Study on the Promises and Challenges of Native Top- Sparse Attention
Di Xiu, Hongyin Tang, Bolin Rong +4
Large Language Models (LLMs) are increasingly prevalent in the field of long-context modeling, however, their inference computational costs have become a critical bottleneck hinder…
OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation
Han Li, Xinyu Peng, Yaoming Wang +7
We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a novel, pure decoder-only transformer architecture. Our fr…
Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns
Xuemiao Zhang, Can Ren, Chengying Tu +5
Recent progress in large reasoning models for challenging mathematical reasoning has been driven by reinforcement learning (RL). Incorporating long chain-of-thought (CoT) data duri…
LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points
Xuemiao Zhang, Can Ren, Chengying Tu +4
The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a novel knowledg…
Large-Scale Diverse Synthesis for Mid-Training
Xuemiao Zhang, Chengying Tu, Can Ren +4
The scarcity of high-quality, knowledge-intensive training data hinders the development of large language models (LLMs), as traditional corpora provide limited information. Previou…
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis
Zihao Wei, Liang Pang, Jiahao Liu +7
Test-time scaling via explicit reasoning trajectories significantly boosts large language model (LLM) performance but often triggers overthinking. To explore this, we analyze reaso…
Libra: Assessing and Improving Reward Model by Learning to Think
Meng Zhou, Bei Li, Jiahao Liu +5
Reinforcement learning (RL) has significantly improved the reasoning ability of large language models. However, current reward models underperform in challenging reasoning scenario…
Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs
Zhengyu Chen, Siqi Wang, Teng Xiao +5
Traditional scaling laws in natural language processing suggest that increasing model size and training data enhances performance. However, recent studies reveal deviations, partic…
GradPower: Powering Gradients for Faster Language Model Pre-Training
Jinbo Wang, Mingze Wang, Jiaqi Zhang +5
We propose GradPower, a lightweight gradient-transformation technique for accelerating language model pre-training. Given a gradient vector , GradPower first applies the…
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
Hexiang Tan, Fei Sun, Sha Liu +8
As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness. However, existing detection…
Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective
Deyang Kong, Qi Guo, Xiangyu Xi +5
Reinforcement learning exhibits potential in enhancing the reasoning abilities of large language models, yet it is hard to scale for the low sample efficiency during the rollout ph…
Dynamic Fisher-weighted Model Merging via Bayesian Optimization
Sanwoo Lee, Jiahao Liu, Qifan Wang +3
The fine-tuning of pre-trained language models has resulted in the widespread availability of task-specific models. Model merging offers an efficient way to create multi-task model…
NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
Lanrui Wang, Mingyu Zheng, Hongyin Tang +5
Processing structured tabular data, particularly large and lengthy tables, constitutes a fundamental yet challenging task for large language models (LLMs). However, existing long-c…
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity
Xiangyu Xi, Deyang Kong, Jian Yang +7
Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and th…
AgentRefine: Enhancing Agent Generalization through Refinement Tuning
Dayuan Fu, Keqing He, Yejie Wang +7
Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial…
Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective
Ruichen Shao, Bei Li, Gangao Liu +5
Direct Preference Optimization (DPO) has gained attention as an efficient alternative to reinforcement learning from human feedback (RLHF) for aligning large language models (LLMs)…
FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy
Xuemiao Zhang, Feiyu Duan, Liangyu Xu +5
Large language models (LLMs) have significantly advanced human language understanding and generation, with pretraining data quality and organization being crucial to their performa…
FIRE: Flexible Integration of Data Quality Ratings for Effective Pre-Training
Liangyu Xu, Xuemiao Zhang, Feiyu Duan +4
Selecting high-quality data can improve the pretraining efficiency of large language models (LLMs). Existing methods generally rely on heuristic techniques or single quality signal…