Papers

• Artificial Entanglement in the Fine-Tuning of Large Language Models (2026)
  Min Chen et al.
  1.94
• Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation (2026)
  Pingzhi Tang et al.
  1.94
• Training Data Efficiency in Multimodal Process Reward Models (2026)
  Jinyuan Li et al.
  1.94
• TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models (2026)
  Chansung Park et al.
  1.94
• Understanding and Enforcing Weight Disentanglement in Task Arithmetic (2026)
  Shangge Liu et al.
  1.94
• Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction (2026)
  Zhong Guan et al.
  1.94
• Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention (2026)
  Jing Huang et al.
  1.94
• AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning (2025)
  Yang Chen et al.
  1.28
• T-LoRA: Single Image Diffusion Model Customization Without Overfitting (2025)
  Vera Soboleva et al.
  1.28
• On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification (2025)
  Yongliang Wu et al.
  1.28
• SimpleFold: Folding Proteins is Simpler than You Think (2025)
  Yuyang Wang et al.
  1.28
• The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N Sampling via max@k Optimisation (2025)
  Farid Bagirov et al.
  1.28