13 papers · 1 filter
LLM Priors for ERM over Programs
Shivam Singhal, Priyadarsi Mishra, Eran Malach +1
We study program-learning methods that are efficient in both samples and computation. Classical learning theory suggests that when the target admits a short program description, fo…
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
Parsa Mirtaheri, Ezra Edelman, Samy Jelassi +2
Inference-time computation has emerged as a promising scaling axis for improving large language model reasoning. However, despite yielding impressive performance, the optimal alloc…
How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Nikolaos Tsilivis, Eran Malach, Karen Ullrich +1
Recent advances in reasoning domains with neural networks have primarily been enabled by a training recipe that optimizes Large Language Models, previously trained to predict the n…
To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models
Eran Malach, Omid Saremi, Sinead Williamson +5
State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation,…
To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning
Tian Qin, David Alvarez-Melis, Samy Jelassi +1
Recent advancements in large language models (LLMs) have significantly improved their reasoning abilities, particularly through techniques involving search and backtracking. Backtr…
Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers
Xianhang Li, Chen Huang, Chun-Liang Li +4
Video Joint Embedding Predictive Architectures (V-JEPA) learn generalizable off-the-shelf video representation by predicting masked regions in latent space with an exponential movi…