Best AI papers explained

Podcast készítő Enoch H. Kang

550 Epizód

Self-Evolving Curriculum for LLM Reasoning
Közzétéve: 2025. 05. 25.
Online Decision-Focused Learning in Dynamic Environments
Közzétéve: 2025. 05. 25.
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
Közzétéve: 2025. 05. 25.
Reward Shaping from Confounded Offline Data
Közzétéve: 2025. 05. 25.
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning
Közzétéve: 2025. 05. 25.
Understanding Best-of-N Language Model Alignment
Közzétéve: 2025. 05. 25.
Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent
Közzétéve: 2025. 05. 24.
Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Közzétéve: 2025. 05. 24.
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Közzétéve: 2025. 05. 24.
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Közzétéve: 2025. 05. 24.
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Közzétéve: 2025. 05. 24.
Automated Social Science: A Structural Causal Model-Based Approach
Közzétéve: 2025. 05. 24.
Causal Interpretation of Transformer Self-Attention
Közzétéve: 2025. 05. 24.
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Közzétéve: 2025. 05. 24.
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Közzétéve: 2025. 05. 24.
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Közzétéve: 2025. 05. 24.
Prompts from Reinforcement Learning (PRL)
Közzétéve: 2025. 05. 24.
Logits are All We Need to Adapt Closed Models
Közzétéve: 2025. 05. 24.
Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Közzétéve: 2025. 05. 23.
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Közzétéve: 2025. 05. 23.

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550 Epizód

Self-Evolving Curriculum for LLM Reasoning

Online Decision-Focused Learning in Dynamic Environments

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain

Reward Shaping from Confounded Offline Data

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning

Understanding Best-of-N Language Model Alignment

Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch

Automated Social Science: A Structural Causal Model-Based Approach

Causal Interpretation of Transformer Self-Attention

A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Prompts from Reinforcement Learning (PRL)

Logits are All We Need to Adapt Closed Models

Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model