Deeper Learning

Using Cognitive Models to Reveal Value Trade-offs in Language Models

By: Sonia Murthy and Peng Qian

The authors use a leading cognitive model of value trade-off in polite speech to systematically examine how post-training choices like reasoning budget and alignment recipes might be affecting value trade-offs in language models.

• Preprint

ANN-like Synapses in the Brain Mediate Online Reinforcement Learning

By: Shun Li

The authors show that a type of synapses in the brain challenges a long-held assumption about synaptic plasticity rules. These synapses switch between more excitatory and more inhibitory in an experience-dependent manner, and contribute to online dopamine updates during reinforcement learning.

• Preprint

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

By: Zhaolin Gao

Gao and collaborators propose a new RL algorithm that estimates the optimal value function offline from the reference policy and performs on-policy updates using only one generation per prompt.

• Preprint
• GitHub

Solvable Model of In-Context Learning Using Linear Attention

By: Mary Letey

This work provides a sharp characterization of in-context learning (ICL) in an analytically-solvable model, which offers insights into the sample complexity and data quality requirements for ICL to happen. These insights can be applied to more complex, realistic architectures.

• Paper
• Code

Flow Equivariant Recurrent Neural Networks

By: T. Anderson Keller

The author introduces the first flow equivariant models that respect motion symmetries, leading to significantly improved generalization and sequence modeling.

• Preprint
• Code

The Hidden Linear Structure in Diffusion Models and its Application in Analytical Teleportation

By: Binxu Wang and John J. Vastola

Diffusion models are powerful generative frameworks that iteratively denoise white noise into structured data via learned score functions. Through theory and experiments, the authors demonstrate that these score functions are dominated by a linear Gaussian component.

• Preprint
• OpenReview

Scaling Offline Reinforcement Learning at Test Time

By: Nicolas Espinosa-Dice

Kempner researchers present a new algorithm for offline reinforcement learning that features a key property: self-consistency.

• Preprint
• Code

Characterization and Mitigation of Training Instabilities in Microscaling Formats

By: Nikhil Anand and Chloe Huangyuan Su

The authors uncover consistent training instabilities when using new, highly efficient low-precision formats, which has implications for the development of next-generation AI. By pinpointing the root causes of these failures and demonstrating effective mitigation strategies, this work offers crucial insights into enabling more cost-effective and scalable model training on future hardware.

• Preprint
• Code

What Data Assumptions Come With Your SAE?

By: Sai Sumedh R. Hindupur*, Ekdeep Singh Lubana*, Thomas Fel*, Demba Ba

The authors show that SAEs are inherently biased toward detecting only a subset of concepts in model activations shaped by their internal assumptions, highlighting the need for concept geometry-aware design of novel SAE architectures.

• Preprint
• Code (synthetic expt.)
• Code (formal lang. expt.)
• Code (vision expt.)

ATOMICA: Learning Universal Representations of Molecular Interactions

By: Ada Fang and Marinka Zitnik

The authors present ATOMICA, a representation learning model that captures intermolecular interactions across all molecular modalities—proteins, nucleic acids, small molecules, and ions—at atomic resolution.

• Paper
• GitHub
• Huggingface