Best AI papers explained
Un pódcast de Enoch H. Kang
550 Episodo
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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Publicado: 22/7/2025 -
The Invisible Leash: Why RLVR May Not Escape Its Origin
Publicado: 20/7/2025 -
Language Model Personalization via Reward Factorization
Publicado: 20/7/2025 -
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Publicado: 18/7/2025 -
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Publicado: 17/7/2025 -
Soft Best-of-n Sampling for Model Alignment
Publicado: 16/7/2025 -
On Temporal Credit Assignment and Data-Efficient Reinforcement Learning
Publicado: 15/7/2025 -
Bradley–Terry and Multi-Objective Reward Modeling Are Complementary
Publicado: 15/7/2025 -
Probing Foundation Models for World Models
Publicado: 15/7/2025 -
GenAI-Powered Statistical Inference (with Unstructured Data)
Publicado: 14/7/2025 -
Interpretable Reward Modeling with Active Concept Bottlenecks
Publicado: 14/7/2025 -
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Publicado: 14/7/2025 -
A Collectivist, Economic Perspective on AI
Publicado: 14/7/2025 -
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
Publicado: 12/7/2025 -
The Winner's Curse in Data-Driven Decisions
Publicado: 11/7/2025 -
SPIRAL: Self-Play for Reasoning Through Zero-Sum Games
Publicado: 11/7/2025 -
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Publicado: 11/7/2025 -
Aligning Learning and Endogenous Decision-Making
Publicado: 11/7/2025 -
Reliable Statistical Inference with Synthetic Data from Large Language Models
Publicado: 11/7/2025 -
Multi-Turn Reinforcement Learning from Human Preference Feedback
Publicado: 10/7/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
