7 papers
Statistical Learning from Attribution Sets
Lorne Applebaum, Robert Busa-Fekete, August Y. Chen +3
We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailab…
Curriculum Learning-Guided Progressive Distillation in Large Language Models
Jincheng Cao, Fanzhi Zeng, Leqi Liu +1
Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models. Existing distillation appro…
Aletheia tackles FirstProof autonomously
Tony Feng, Junehyuk Jung, Sang-hyun Kim +14
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed t…
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…
Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors
Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3
Learning disentangled representations, where distinct factors of variation are captured by independent latent variables, is a central goal in machine learning. The dominant approac…
Learning Mixtures of Experts with EM: A Mirror Descent Perspective
Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi
Classical Mixtures of Experts (MoE) are Machine Learning models that involve partitioning the input space, with a separate "expert" model trained on each partition. Recently, MoE-b…