:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chawla, Aviral, Hall, Galen, Lovato, Juniper
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.23164
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Automatically Finding Rule-Based Neurons in OthelloGPT
by: Singh, Aditya, et al.
Published: (2025)

Emergent Stack Representations in Modeling Counter Languages Using Transformers
by: Tiwari, Utkarsh, et al.
Published: (2025)

Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT
by: He, Zhengfu, et al.
Published: (2024)

Provable Generalization in Overparameterized Neural Nets
by: Dhingra, Aviral
Published: (2025)

A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases
by: Jain, Ananya, et al.
Published: (2024)

MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games
by: Wu, Ti-Rong, et al.
Published: (2023)

Gradient Descent Efficiency Index
by: Dhingra, Aviral
Published: (2024)

Dimension-Free Bounds for Generalized First-Order Methods via Gaussian Coupling
by: Reeves, Galen
Published: (2025)

A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation
by: Srivastava, Aviral, et al.
Published: (2024)

Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
by: Fung, Lloyd, et al.
Published: (2024)

phepy: Visual Benchmarks and Improvements for Out-of-Distribution Detectors
by: Tyree, Juniper, et al.
Published: (2025)

Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting
by: Chawla, Freyaa, et al.
Published: (2026)

AnyLoss: Transforming Classification Metrics into Loss Functions
by: Han, Doheon, et al.
Published: (2024)

Unfamiliar Finetuning Examples Control How Language Models Hallucinate
by: Kang, Katie, et al.
Published: (2024)

Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
by: Bai, Hao, et al.
Published: (2025)

TabPFN Through The Looking Glass: An interpretability study of TabPFN and its internal representations
by: Gupta, Aviral, et al.
Published: (2026)

BIRD: Behavior Induction via Representation-structure Distillation
by: Pogoncheff, Galen, et al.
Published: (2025)

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
by: Nakamoto, Mitsuhiko, et al.
Published: (2024)

Ethical Frameworks for Conducting Social Challenge Studies
by: Sen, Protiva, et al.
Published: (2025)

Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
by: Wang, Zhi, et al.
Published: (2024)

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
by: Snell, Charlie, et al.
Published: (2024)

Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models
by: Le, Khiem, et al.
Published: (2026)

Information-Theoretic Proofs for Diffusion Sampling
by: Reeves, Galen, et al.
Published: (2025)

What Does Flow Matching Bring To TD Learning?
by: Agrawalla, Bhavya, et al.
Published: (2026)

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
by: Bai, Hao, et al.
Published: (2024)

Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
by: Qian, Fuyuan, et al.
Published: (2026)

Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art
by: Lovato, Juniper, et al.
Published: (2024)

Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL
by: Wu, Ian, et al.
Published: (2026)

Revisiting the Othello World Model Hypothesis
by: Yuan, Yifei, et al.
Published: (2025)

Generative Verifiers: Reward Modeling as Next-Token Prediction
by: Zhang, Lunjun, et al.
Published: (2024)

Recursive Introspection: Teaching Language Model Agents How to Self-Improve
by: Qu, Yuxiao, et al.
Published: (2024)

Contextual Latent World Models for Offline Meta Reinforcement Learning
by: Nakheai, Mohammadreza, et al.
Published: (2026)

MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
by: Rimon, Zohar, et al.
Published: (2024)

Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
by: Carvalho, Tânia, et al.
Published: (2022)

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
by: Qu, Yuxiao, et al.
Published: (2025)

Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning
by: Kontogiannis, Alexandros, et al.
Published: (2024)

Vision-Language Models Provide Promptable Representations for Reinforcement Learning
by: Chen, William, et al.
Published: (2024)

Scaling Test-Time Compute Without Verification or RL is Suboptimal
by: Setlur, Amrith, et al.
Published: (2025)

floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
by: Agrawalla, Bhavya, et al.
Published: (2025)

Is Value Learning Really the Main Bottleneck in Offline RL?
by: Park, Seohong, et al.
Published: (2024)