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cs.LG2026
A Relative-Budget Theory for Reinforcement Learning with Verifiable Rewards in Large Language Model Reasoning
Akifumi Wachi, Hirota Kinoshita, Shokichi Takakura +2
Reinforcement learning (RL) is a dominant paradigm for improving the reasoning abilities of large language models, yet its effectiveness varies across tasks and compute budgets. We…
cs.LG2025
Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency
Naoki Nishikawa, Rei Higuchi, Taiji Suzuki
Linear attention has attracted interest as a computationally efficient approximation to softmax attention, especially for long sequences. Recent studies have explored distilling so…
cs.LG2025
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
Rei Higuchi, Taiji Suzuki
Aligning large language models (LLMs) with human preferences is crucial for safe deployment, yet existing methods assume specific preference models like Bradley-Terry model. This a…