13 citations · 27 across the 13 of their papers we have counts for
7 papers · 1 filter
QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards
Rongzhi Zhang, Rui Feng, Zhihan Zhang +8
Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as…
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang +16
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce ne…
END: Early Noise Dropping for Efficient and Effective Context Denoising
Hongye Jin, Pei Chen, Jingfeng Yang +11
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, they are often distracted by irrelevant or…
Scaling Laws for Predicting Downstream Performance in LLMs
Yangyi Chen, Binxuan Huang, Yifan Gao +3
Precise estimation of downstream performance in large language models (LLMs) prior to training is essential for guiding their development process. Scaling laws analysis utilizes th…
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang +5
It is well known that LLMs cannot generalize well to long contexts whose lengths are larger than the training sequence length. This poses challenges when employing LLMs for process…
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang +6
Recent works have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. However, creating su…
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length
Hongye Jin, Xiaotian Han, Jingfeng Yang +3
The evolving sophistication and intricacies of Large Language Models (LLMs) yield unprecedented advancements, yet they simultaneously demand considerable computational resources an…