1 citations · 1 across the 8 of their papers we have counts for
7 papers · 1 filter
Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL
Qinwei Ma, Jingzhe Shi, Simin Fan +3
ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in ELBO-based RL has large impact…
Position: The Inevitable End of One-Architecture-Fits-All-Domains in Time Series Forecasting
Qinwei Ma, Jingzhe Shi, Jiahao Qiu +1
Recent work has questioned the effectiveness and robustness of neural network architectures for time series forecasting tasks. We summarize these concerns and analyze groundly thei…
PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Wanjia Zhao, Qinwei Ma, Jingzhe Shi +7
Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively explored. Most existing physics benchmarks eva…
Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm
Kaisen Yang, Tinghe Zhang, Rushi Shah +4
Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or split across separate component…
Gradient Imbalance in Direct Preference Optimization
Qinwei Ma, Jingzhe Shi, Can Jin +3
Direct Preference Optimization (DPO) has been proposed as a promising alternative to Proximal Policy Optimization (PPO) based Reinforcement Learning with Human Feedback (RLHF). How…
Intrinsic Entropy of Context Length Scaling in LLMs
Jingzhe Shi, Qinwei Ma, Hongyi Liu +3
Long Context Language Models have drawn great attention in the past few years. There has been work discussing the impact of long context on Language Model performance: some find th…
Scaling Law for Time Series Forecasting
Jingzhe Shi, Qinwei Ma, Huan Ma +1
Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning. Yet, studies on time series forecasting…