7 papers
VALG: An Agentic System for ML Theory Research
Dechen Zhang, Xuan Tang, Xinxiang Yin +3
Machine learning theory studies learning procedures through mathematical setups in which the data model, training protocol, oracle access, loss, metric, and randomness define the p…
Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction
Xingwu Chen, Zhanqiu Zhang, Yiwen Guo +1
While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in multi-turn interactions. Specif…
Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
Xingwu Chen, Miao Lu, Beining Wu +1
Using more test-time computation during language model inference, such as generating more intermediate thoughts or sampling multiple candidate answers, has proven effective in sign…
Reshaping Reasoning in LLMs: A Theoretical Analysis of RL Training Dynamics through Pattern Selection
Xingwu Chen, Tianle Li, Difan Zou
While reinforcement learning (RL) demonstrated remarkable success in enhancing the reasoning capabilities of language models, the training dynamics of RL in LLMs remain unclear. In…
On the Robustness of Transformers against Context Hijacking for Linear Classification
Tianle Li, Chenyang Zhang, Xingwu Chen +2
Transformer-based Large Language Models (LLMs) have demonstrated powerful in-context learning capabilities. However, their predictions can be disrupted by factually correct context…
How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression
Xingwu Chen, Lei Zhao, Difan Zou
Despite the remarkable success of transformer-based models in various real-world tasks, their underlying mechanisms remain poorly understood. Recent studies have suggested that tra…
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks
Xingwu Chen, Difan Zou
We study the capabilities of the transformer architecture with varying depth. Specifically, we designed a novel set of sequence learning tasks to systematically evaluate and compre…