Best AI papers explained

Podcast készítő Enoch H. Kang

550 Epizód

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Közzétéve: 2025. 07. 22.
The Invisible Leash: Why RLVR May Not Escape Its Origin
Közzétéve: 2025. 07. 20.
Language Model Personalization via Reward Factorization
Közzétéve: 2025. 07. 20.
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Közzétéve: 2025. 07. 18.
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Közzétéve: 2025. 07. 17.
Soft Best-of-n Sampling for Model Alignment
Közzétéve: 2025. 07. 16.
On Temporal Credit Assignment and Data-Efficient Reinforcement Learning
Közzétéve: 2025. 07. 15.
Bradley–Terry and Multi-Objective Reward Modeling Are Complementary
Közzétéve: 2025. 07. 15.
Probing Foundation Models for World Models
Közzétéve: 2025. 07. 15.
GenAI-Powered Statistical Inference (with Unstructured Data)
Közzétéve: 2025. 07. 14.
Interpretable Reward Modeling with Active Concept Bottlenecks
Közzétéve: 2025. 07. 14.
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Közzétéve: 2025. 07. 14.
A Collectivist, Economic Perspective on AI
Közzétéve: 2025. 07. 14.
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
Közzétéve: 2025. 07. 12.
The Winner's Curse in Data-Driven Decisions
Közzétéve: 2025. 07. 11.
SPIRAL: Self-Play for Reasoning Through Zero-Sum Games
Közzétéve: 2025. 07. 11.
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Közzétéve: 2025. 07. 11.
Aligning Learning and Endogenous Decision-Making
Közzétéve: 2025. 07. 11.
Reliable Statistical Inference with Synthetic Data from Large Language Models
Közzétéve: 2025. 07. 11.
Multi-Turn Reinforcement Learning from Human Preference Feedback
Közzétéve: 2025. 07. 10.

9 / 28

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

Visit the podcast's native language site

550 Epizód

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

The Invisible Leash: Why RLVR May Not Escape Its Origin

Language Model Personalization via Reward Factorization

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective

Soft Best-of-n Sampling for Model Alignment

On Temporal Credit Assignment and Data-Efficient Reinforcement Learning

Bradley–Terry and Multi-Objective Reward Modeling Are Complementary

Probing Foundation Models for World Models

GenAI-Powered Statistical Inference (with Unstructured Data)

Interpretable Reward Modeling with Active Concept Bottlenecks

PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications

A Collectivist, Economic Perspective on AI

Textual Bayes: Quantifying Uncertainty in LLM-Based Systems

The Winner's Curse in Data-Driven Decisions

SPIRAL: Self-Play for Reasoning Through Zero-Sum Games

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence

Aligning Learning and Endogenous Decision-Making

Reliable Statistical Inference with Synthetic Data from Large Language Models

Multi-Turn Reinforcement Learning from Human Preference Feedback