13 papers
An Empirical Study of Parameter-Efficient Fine-Tuning for Mixture-of-Experts LLMs
Minglei Li, Peng Ye, Haonan He +3
Parameter-efficient fine-tuning (PEFT) methods are critical for adapting large models under resource-constrained settings. However, as mixture-of-experts (MoE) architectures, which…
Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
Cunchun Li, Haonan He, Yifan Gao +4
Supervised fine-tuning (SFT) learns most aggressively from tokens that the model deems least likely. This helps acquire new behaviors, but also amplifies noisy or conflicting super…
LoRA-GA: Low Rank Adaptation with Multi-step Gradient Adaptive Alignment
Haonan He, Xinyue Fan
Low-Rank Adaptation (LoRA) is a prominent fine-tuning method for large models, achieving competitive performance with reduced memory overhead. However, a persistent performance gap…
SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
Haonan He, Haodi Lei, Yun Luo +13
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-co…
MoE-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation
Qingyu Yang, Haonan He, Minglei Li +4
Mixture-of-Experts (MoE) architectures have been widely adopted in large language models, yet parameter-efficient fine-tuning (PEFT) for MoE models remains underexplored. Existing…
Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
Lei Bai, Zongsheng Cao, Yang Chen +50
We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling…
Parametric Skills
Xuan Zhao, Haonan He, Qingyu Yang +5
Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, manually authored or extracted…
A Unified Study of LoRA Variants: Taxonomy, Review, Codebase, and Empirical Evaluation
Haonan He, Jingqi Ye, Minglei Li +4
Low-Rank Adaptation (LoRA) is a fundamental parameter-efficient fine-tuning method that balances efficiency and performance in large-scale neural networks. However, the proliferati…
P1: Mastering Physics Olympiads with Reinforcement Learning
Jiacheng Chen, Qianjia Cheng, Fangchen Yu +25
Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand ag…
Scaling Physical Reasoning with the PHYSICS Dataset
Shenghe Zheng, Qianjia Cheng, Junchi Yao +9
Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reaso…
Control-R: Towards controllable test-time scaling
Di Zhang, Weida Wang, Junxian Li +10
This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning…
GoRA: Gradient-driven Adaptive Low Rank Adaptation
Haonan He, Peng Ye, Yuchen Ren +4
Low-Rank Adaptation (LoRA) is a crucial method for efficiently fine-tuning large language models (LLMs), with its effectiveness influenced by two key factors: rank selection and we…
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
Haonan He, Yuchen Ren, Yining Tang +12
Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we intr…