All episodes

You Are What You Eat - AI Alignment Requires Understanding How Data Shapes Structure and Generalisation

Interplay of LLMs in Information Retrieval Evaluation

Trade-Offs Between Tasks Induced by Capacity Constraints Bound the Scope of Intelligence

Toward Efficient Exploration by Large Language Model Agents

Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT

Self-Consuming Generative Models with Curated Data

Bootstrapping Language Models with DPO Implicit Rewards

DeepSeek-Prover-V2: Advancing Formal Reasoning

THINKPRM: Data-Efficient Process Reward Models

Societal Frameworks and LLM Alignment

Risks from Multi-Agent Advanced AI

Causality-Aware Alignment for Large Language Model Debiasing

Reward Models Evaluate Consistency, Not Causality

Causal Rewards for Large Language Model Alignment

Sycophancy to subterfuge: Investigating reward-tampering in large language models

Bidirectional AI Alignment

Why Do Multi-Agent LLM Systems Fail?

LLMs as Greedy Agents: RL Fine-tuning for Decision-Making

LLM Feedback Loops and the Lock-in Hypothesis

Representational Alignment Drives Effective Teaching and Learning

Adaptive Parallel Reasoning with Language Models

AI: Rewiring the Flow of Ideas and Human Knowledge

Learning and Equilibrium with Ranking Feedback

Designing Human-AI Collaboration: A Sufficient-Statistic Approach

GOAT: Generative Adversarial Training for Human-AI Coordination

π0.5: Generalization in Robotic Manipulation via Diverse Data

NoWag: Unified Compression for Large Language Models

Optimal Tool Calls in Language Model Reasoning

Data Selection for Empirical Risk Minimization

LoRe: Low-Rank Reward Modeling for Personalized LLMs

ParaPO: Reducing Language Model Verbatim Reproduction

Test-Time RL: Self-Evolving LLMs via Majority Voting Rewards

Tina: Tiny LoRA Reasoning Models

Evaluating large language models in theory of mind tasks

QUEST: Quality Sampling for Machine Translation

Offline Preference Learning via Simulated Trajectory Feedback

Reasoning Elicitation in Language Models via Counterfactual Feedback

Eliciting Human Preferences with Language Models

Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning

γ-Bench: Evaluating LLMs in Multi-Agent Games

DRAFT: Self-Driven LLM Tool Mastery via Documentation Refinement

Optimal Prediction Sets for Enhanced Human-AI Accuracy

Self-Correction via Reinforcement Learning for Language Models

Tractable Multi-Agent Reinforcement Learning through Behavioral Economics

Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement

Iterative Nash Policy Optimization for Language Model Alignment

SycEval: Benchmarking LLM Sycophancy in Mathematics and Medicine

Stack AI: Democratizing Enterprise AI Development

Evaluating Modern Recommender Systems: Challenges and Future Directions

AI in the Enterprise: Seven Lessons from Frontier Companies by OpenAI

Discussion: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

AI Agent Protocols and Human Preference

Cross-Environment Cooperation for Zero-Shot Multi-Agent Coordination

Sutton and Silver: The Era of Experience: Learning Beyond Human Data

Sample, Don't Search: Rethinking Test-Time Alignment for Language Models

AI Agents: Echoes of Past Technology Pivots?

Minimalist LLM Reasoning: Rejection Sampling to Reinforcement

Securing the Model Context Protocol in Enterprise Environments

Improving Multi-Turn Tool Use with Reinforcement Learning

Cultural Knowledge Conservation and Control in Large Language Models

Data Quality, Repetition, and Scaling of Language Models

Compute-Optimal Scaling Laws for Language Models Revisited

Concise Reasoning via Reinforcement Learning

Throughput Limits for LLM Inference and AI Agent Scheduling

RL Post-training Amplifies Pretraining Behaviors in Language Models

Fast Adaptation of Behavioral Foundation Models

Proprietary Reward Models: Sustaining Advantage in Agentic AI

Why Multi-Agent LLM Systems Fail: A Comprehensive Study

Play2Prompt: Zero-Shot Tool Instruction Optimization via Tool Play

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

API and GUI Agents: Divergence, Convergence, and Hybrid Approaches

AI, Chess, and Competitive Advantage: Substitution and Complementation

Knowledge of the Firm and Replication of Technology

Firm Resources and Sustained Competitive Advantage

Evaluating Pharmaceutical Marketing to Physicians with Panel Data

Theory of the firm in the era of Agents

Large Language Models: An Applied Econometric Framework

Evaluating the World Model Implicit in a Generative Model

Machine Learning for Hypothesis Generation in Social Science

Active Learning for Moral Preference Elicitation: Challenges and Nuances

Gradient-Based Surveys for Nonparametric Discrete Choice Experiments

Explainable Data-driven Share-of-choice Product Line Design Optimization

The More You Ask, the Less You Get: When Additional Questions Hurt External Validity

Conjoint topics from Handbook of Marketing Analytics: Methods and Applications

Choice-Based Conjoint Analysis: Methods and Applications

Beyond Conjoint Analysis: The Future of Preference Measurement

An Optimization Framework for Adaptive Questionnaire Design

Adaptive Self-Explication of Multiattribute Preferences

Conjoint Analysis: Methods, Applications, and Recent Developments

Current Issues and a “Wish List” for Conjoint Analysis

Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis

Adaptive Polyhedral Methods for Conjoint Analysis

MSL: Enhancing LLM Recommenders via Masked Softmax Loss

Self-Supervised Deep Reinforcement Learning for Optimal Question Ranking

Adaptive Language Elicitation for Latent Information Discovery

LLM Persona Bias: Promise and Peril in Simulation

AutoTools: Automating Tool Use for Large Language Models

Tool Learning with Large Language Models: A Comprehensive Survey

All Roads Lead to Likelihood: RL for Fine-Tuning Value

ATLAS: Tuning Agents via Critical Step Learning