125 citations · 346 across the 41 of their papers we have counts for
11 papers · 1 filter
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,…
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…
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…
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…
A Taxonomy of Transcendence
Natalie Abreu, Edwin Zhang, Eran Malach +1
Although language models are trained to mimic humans, the resulting systems display capabilities beyond the scope of any one person. To understand this phenomenon, we use a control…
Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
Tian Qin, Core Francisco Park, Mujin Kwun +5
Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs, especially with reinforcement learning (RL) methods such as GRPO sho…