338 citations · 367 across the 22 of their papers we have counts for
22 papers
Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
Siyuan Huang, Pengyu Cheng, Haotian Liu +10
LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task…
Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill
Tao Chen, Gangwei Jiang, Pengyu Cheng +10
Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current rew…
On the Role of Reasoning Patterns in the Generalization Discrepancy of Long Chain-of-Thought Supervised Fine-Tuning
Zhaoyi Li, Xiangyu Xi, Zhengyu Chen +6
Supervised Fine-Tuning (SFT) on long Chain-of-Thought (CoT) trajectories has become a pivotal phase in building large reasoning models. However, how CoT trajectories from different…
Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
Zhaoyi Li, Jiatong Li, Gangwei Jiang +3
Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems. However, recent studies reveal a sharp perfor…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning
Gangwei Jiang, Yahui Liu, Zhaoyi Li +5
Recent advances in reasoning with large language models (LLMs) have popularized Long Chain-of-Thought (LCoT), a strategy that encourages deliberate and step-by-step reasoning befor…
Learning to Substitute Components for Compositional Generalization
Zhaoyi Li, Gangwei Jiang, Chenwang Wu +3
Despite the rising prevalence of neural language models, recent empirical evidence suggests their deficiency in compositional generalization. One of the current de-facto solutions…
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning
Gangwei Jiang, Caigao Jiang, Zhaoyi Li +5
Catastrophic forgetting (CF) poses a significant challenge in machine learning, where a model forgets previously learned information upon learning new tasks. Despite the advanced c…
GTM: A General Time-series Model for Enhanced Representation Learning of Time-Series Data
Cheng He, Xu Huang, Gangwei Jiang +7
Despite recent progress in time-series foundation models, challenges persist in improving representation learning and adapting to diverse downstream tasks. We introduce a General T…
Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning
Chenyuan Wu, Gangwei Jiang, Defu Lian
Lifelong prompt tuning has significantly advanced parameter-efficient lifelong learning with its efficiency and minimal storage demands on various tasks. Our empirical studies, how…
DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models
Fan Zhou, Siqiao Xue, Danrui Qi +8
Large language models (LLMs) becomes the dominant paradigm for the challenging task of text-to-SQL. LLM-empowered text-to-SQL methods are typically categorized into prompting-based…
Refine Large Language Model Fine-tuning via Instruction Vector
Gangwei Jiang, Zhaoyi Li, Defu Lian +1
Fine-tuning large language models (LLMs) can cause them to lose their general capabilities. However, the intrinsic mechanisms behind such forgetting remain unexplored. In this pape…
Understanding and Patching Compositional Reasoning in LLMs
Zhaoyi Li, Gangwei Jiang, Hong Xie +3
LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional re…
On the Opportunities of Green Computing: A Survey
You Zhou, Xiujing Lin, Xiang Zhang +38
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including…
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
Gangwei Jiang, Caigao Jiang, Siqiao Xue +4
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model…
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Siqiao Xue, Yan Wang, Zhixuan Chu +7
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In re…
Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
Qi Liu, Zhilong Zhou, Gangwei Jiang +2
Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g.…
Continual Learning in Predictive Autoscaling
Hongyan Hao, Zhixuan Chu, Shiyi Zhu +7
Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. Ho…
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
Jin Chen, Zheng Liu, Xu Huang +9
The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of…
Self-Supervised Text Erasing with Controllable Image Synthesis
Gangwei Jiang, Shiyao Wang, Tiezheng Ge +3
Recent efforts on scene text erasing have shown promising results. However, existing methods require rich yet costly label annotations to obtain robust models, which limits the use…
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Jin Chen, Tiezheng Ge, Gangwei Jiang +3
Ad creatives are one of the prominent mediums for online e-commerce advertisements. Ad creatives with enjoyable visual appearance may increase the click-through rate (CTR) of produ…
Automated Creative Optimization for E-Commerce Advertising
Jin Chen, Ju Xu, Gangwei Jiang +4
Advertising creatives are ubiquitous in E-commerce advertisements and aesthetic creatives may improve the click-through rate (CTR) of the products. Nowadays smart advertisement pla…