All episodes
Selecting Belief-State Approximations in Simulators with Latent States
Latent Collaboration in Multi-Agent Systems
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
DELTA: How Does RL Unlock and Transfer New Algorithms in LLMs?
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
Ilya Sutskever – We're moving from the age of scaling to the age of research
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Natural emergent misalignment from reward hacking in production RL
Evolution Strategies at the Hyperscale
The Path Not Taken: RLVR Provably Learns Off the Principals
Back to Basics: Let Denoising Generative Models Denoise
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
Black-Box On-Policy Distillation of Large Language Models
Solving a million step LLM task with zero errors
Not All Thoughts Matter: Selective Attention for Efficient Reasoning
Sample-Efficient Parametric Learning from Natural Language
Bayesian Optimization in Language space: An Eval-Efficient AI Self-Improvement Framework
Context Engineering: Sessions, Memory
The Era of Agentic Organization: Learning to Organize with Language Models
Understanding neural networks through sparse circuits
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
Multi-Agent Evolve: LLM Self-Improvement Through Co-Evolution
LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
PREFDISCO: Evaluating Proactive Personalization through Interactive Preference Discovery
Reusing pre-training data at test time is a compute multiplier
Scaling Agent Learning via Experience Synthesis
Continuous Autoregressive Language Models
Toward a Theory of Agents as Tool-Use Decision-Makers
Nested Learning: The Illusion of Deep Learning Architectures
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Beyond a million tokens: benchmarking and enhancing long-term memory in llms
Agentic Economic Modeling
Emergent Introspective Awareness in Large Language Models
Can Large reasoning models self-train?
ALITA-G: Self-Evolving Generative Agent for Agent Generation
Self-improving LLM agents at test-time
Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Language models are injective and hence invertible
ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
RLAD: Training LLMs to Discover Abstractions
How to Train Your Advisor: Steering Black-Box LLMs with ADVISOR MODELS
Self-improving LLM agents at Test-Time
KL-Regularized Reinforcement Learning is designed to Mode Collapse
How do LLMs use their depth?
Thought Communication in Multiagent Collaboration
Reasoning with Sampling: Base Models Outperform RL
Continual Learning via Sparse Memory Finetuning
Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences
The Coverage Principle: How Pre-Training Enables Post-Training
The Era of Real-World Human Interaction: RL from User Conversations
Agent Learning via Early Experience
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior
A Definition of AGI
Provably Learning from Language Feedback
In-Context Learning for Pure Exploration
On the Role of Preference Variance in Preference Optimization
Training LLM Agents to Empower Humans
Richard Sutton Declares LLMs a Dead End
Demystifying Reinforcement Learning in Agentic Reasoning
Emergent coordination in multi-agent language models
Learning-to-measure: in-context active feature acquisition
Andrej Karpathy's insights: AGI, Intelligence, and Evolution
Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
The attacker moves second: stronger adaptive attacks bypass defenses against LLM jail- Breaks and prompt injections
When can in-context learning generalize out of task distribution?
The Art of Scaling Reinforcement Learning Compute for LLMs
A small number of samples can poison LLMs of any size
Dual Goal Representations
Welcome to the Era of Experience
Value Flows: Flow-Based Distributional Reinforcement Learning
Self-Adapting Language Models
The Markovian Thinker
Moloch’s Bargain: emergent misalignment when LLMs compete for audiences
Transformer Predictor Dynamics and Task Diversity
Base models know how to reason, thinking models learn when
Spectrum tuning: Post-training for distributional coverage and in-context steerability
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
MLPs Learn In-Context on Regression and Classification tasks
Is Pre-Training Truly Better than Meta-Learning?
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
Learning dynamics of LLM finetuning
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
OpenAI Agent Builder and n8n: Orchestrating Reasoning Versus Automating Process
Training Agents Inside of Scalable World Models
Small Language Models are the Future of Agentic AI
Activation Steering in Generative Settings via Contrastive Causal Mediation Analysis
Eliciting Secret Knowledge from Language Models
Temporal difference flow
Personalized reasoning: just-in-time personalization and why LLMs fail at it
Prompt Curriculum Learning for Efficient LLM Post-Training
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward
Learning to summarize user information for personalized reinforcement learning from human feedback
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
LIMI: Less is More for Agency
LoRA Without Regret