1 citations · 3 across the 9 of their papers we have counts for
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Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Qinqing Zheng, Mikael Henaff, Amy Zhang +2
Automatically synthesizing dense rewards from natural language descriptions is a promising paradigm in reinforcement learning (RL), with applications to sparse reward problems, ope…
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
Buu Phan, Brandon Amos, Itai Gat +3
Tokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studi…
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic, Xi Zhang, Brandon Amos +5
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical p…
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi, Yilun Kuang, Brandon Amos +3
Large language models (LLMs) with billions of parameters excel at predicting the next token in a sequence. Recent work computes non-vacuous compression-based generalization bounds…