5 papers · 1 filter
-Balancing for Mixture-of-Experts Training
Lizhang Chen, Jonathan Li, Qi Wang +5
Mixture-of-Experts (MoE) models rely on balanced expert utilization to fully realize their scalability. However, existing load-balancing methods are largely heuristic and operate o…
Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces
Manas Pathak, Xingyao Chen, Shuozhe Li +2
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reason…
Learning Robust Reasoning through Guided Adversarial Self-Play
Shuozhe Li, Vaishnav Tadiparthi, Kwonjoon Lee +6
Reinforcement learning from verifiable rewards (RLVR) produces strong reasoning models, yet they can fail catastrophically when the conditioning context is fallible (e.g., corrupte…
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
Shuozhe Li, Jincheng Cao, Bodun Hu +3
Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while un…
A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization
Shuozhe Li, Du Cheng, Leqi Liu
Learning profitable intraday trading policies from financial time series is challenging due to heavy noise, non-stationarity, and strong cross-sectional dependence among related as…