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cs.LG2026
Explaining Attention with Program Synthesis
Amiri Hayes, Belinda Z Li, Jacob Andreas
A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an ap…
cs.LG2026
Self-CTRL: Self-Consistency Training with Reinforcement Learning
Itamar Pres, Laura Ruis, Melat Ghebreselassie +2
Language models (LMs) that faithfully describe their own behavior can more easily be audited, understood, and trusted by users. This paper describes Self-Consistency Training with…
cs.LG2025
A Hitchhiker's Guide to Scaling Law Estimation
Leshem Choshen, Yang Zhang, Jacob Andreas
Scaling laws predict the loss of a target machine learning model by extrapolating from easier-to-train models with fewer parameters or smaller training sets. This provides an effic…