Accepted abstracts (2026)

Abstracts accepted to FoPt 2026

2026

  1. How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics
    Yurong Chen, Yu He, Michael I. Jordan, and Fan Yao
    2026
  2. INFUSER: Influence-Guided Self-Evolution Improves Reasoning
    Siyu Chen, Miao Lu, Beining Wu, Heejune Sheen, Fengzhuo Zhang, Shuangning Li, Zhiyuan Li, Jose Blanchet, Tianhao Wang, and Zhuoran Yang
    2026
  3. Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
    Ved Sriraman, Peihan Liu, Daniel Hsu, and Adam Block
    2026
  4. Pass@k Learning from Suboptimal Demonstrations
    Chandramauli Chakraborty and Cong Ma
    2026
  5. On the Emergence of Implicit Curriculum in RLVR Learning Dynamics
    Yu Huang, Zixin Wen, Yuejie Chi, Yuting Wei, Aarti Singh, Yingbin Liang, and Yuxin Chen
    2026
  6. VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
    Kijung Jeon, Thuy-Duong Vuong, and Molei Tao
    2026
  7. Betting Loss Beyond Realizability: Limits of Squared Error and Variance-Adaptive L^1 Bound
    Yinan Li and Kwang-Sung Jun
    2026
  8. Reinforcement Learning from Rich Feedback with Distributional DAgger
    Rishabh Agrawal, Jacob Fein-Ashley, and Paria Rashidinejad
    2026
  9. Policy optimization goes beyond sharpening of base models
    Audrey Huang, Sadhika Malladi, Akshay Krishnamurthy, Nan Jiang, and Dylan J Foster
    2026
  10. Reasoning with Sampling: Cutting at Decision Points
    Felix Zhou, Anay Mehrotra, and Quanquan C. Liu
    2026
  11. Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
    Shaddin Dughmi, Mahdi Haghifam, and Yusuf Hakan Kalayci
    2026