All episodes

Thinking Faster by Writing Less: Chain of Draft Reasoning

Meta Plan Optimization for Boosting LLM Agents

L1: Length Controlled Reasoning with Reinforcement Learning

WikiBigEdit: Benchmarking Lifelong Knowledge Editing in LLMs

PLAN-AND-ACT: LLM Agent Planning with Synthetic Data

SEARCH-R1: LLMs Learn to Reason and Search via Reinforcement Learning

The Theory of the Firm: Information, Incentives, and Organization

Four Formalizable Theories of the Firm

Efficient Tool Use with Chain-of-Abstraction Reasoning

CodeTool: Process Supervision for Enhanced LLM Tool Invocation

Evaluating LLM Agents in Multi-Turn Conversations: A Survey

Epistemic Alignment in User-LLM Knowledge Delivery

MCP is (not) all you need

AI, Human Skills, and Competitive Advantage in Chess

Inference-Time Scaling for Generalist Reward Modeling

Optimal Pure Exploration in Linear Bandits via Sampling

Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products

Emergent Symbolic Mechanisms for Reasoning in Large Language Models

Inference-Time Alignment: Coverage, Scaling, and Optimality

Sharpe Ratio-Guided Active Learning for Preference Optimization

Active Learning for Adaptive In-Context Prompt Design

Visual Chain-of-Thought Reasoning for Vision-Language-Action Models

On the Biology of a Large Language Model

Async-TB: Asynchronous Trajectory Balance for Scalable LLM RL

Instacart's Economics Team: A Hybrid Role in Tech

Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework

Why MCP won

SWEET-RL: Training LLM Agents for Collaborative Reasoning

TheoryCoder: Bilevel Planning with Synthesized World Models

Driving Forces in AI: Scaling to 2025 and Beyond (Jason Wei, OpenAI)

Expert Demonstrations for Sequential Decision Making under Heterogeneity

TextGrad: Backpropagating Language Model Feedback for Generative AI Optimization

MemReasoner: Generalizing Language Models on Reasoning-in-a-Haystack Tasks

RAFT: In-Domain Retrieval-Augmented Fine-Tuning for Language Models

Inductive Biases for Exchangeable Sequence Modeling

InverseRLignment: LLM Alignment via Inverse Reinforcement Learning

Prompt-OIRL: Offline Inverse RL for Query-Dependent Prompting

Alignment from Demonstrations for Large Language Models

Q♯: Distributional RL for Optimal LLM Post-Training

Scaling Test-Time Compute Without Verification or RL is Suboptimal

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Revisiting Superficial Alignment Hypothesis

Diagnostic uncertainty: teaching language Models to describe open-ended uncertainty

Language Model Personalization via Reward Factorization

How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

Can Large Language Models Extract Customer Needs as well as Professional Analysts?

Spurlens: finding spurious correlations in Multimodal llms

Improving test-time search with backtrack- Ing Improving test-time search with backtrack- Ing against in-context value verifiersagainst in-context value verifiers

Adaptive elicitation of latent information Using natural language

Document Valuation in LLM Summaries: A Cluster Shapley Approach

s1: simple test time scaling