Best AI papers explained
En podcast av Enoch H. Kang
471 Avsnitt
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An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models
Publicerades: 2025-07-22 -
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Publicerades: 2025-07-22 -
The Invisible Leash: Why RLVR May Not Escape Its Origin
Publicerades: 2025-07-20 -
Language Model Personalization via Reward Factorization
Publicerades: 2025-07-20 -
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Publicerades: 2025-07-18 -
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Publicerades: 2025-07-17 -
Soft Best-of-n Sampling for Model Alignment
Publicerades: 2025-07-16 -
On Temporal Credit Assignment and Data-Efficient Reinforcement Learning
Publicerades: 2025-07-15 -
Bradley–Terry and Multi-Objective Reward Modeling Are Complementary
Publicerades: 2025-07-15 -
Probing Foundation Models for World Models
Publicerades: 2025-07-15 -
GenAI-Powered Statistical Inference (with Unstructured Data)
Publicerades: 2025-07-14 -
Interpretable Reward Modeling with Active Concept Bottlenecks
Publicerades: 2025-07-14 -
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Publicerades: 2025-07-14 -
A Collectivist, Economic Perspective on AI
Publicerades: 2025-07-14 -
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
Publicerades: 2025-07-12 -
The Winner's Curse in Data-Driven Decisions
Publicerades: 2025-07-11 -
SPIRAL: Self-Play for Reasoning Through Zero-Sum Games
Publicerades: 2025-07-11 -
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Publicerades: 2025-07-11 -
Aligning Learning and Endogenous Decision-Making
Publicerades: 2025-07-11 -
Reliable Statistical Inference with Synthetic Data from Large Language Models
Publicerades: 2025-07-11
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.