Best AI papers explained
Un pódcast de Enoch H. Kang
550 Episodo
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GOAT: Generative Adversarial Training for Human-AI Coordination
Publicado: 27/4/2025 -
π0.5: Generalization in Robotic Manipulation via Diverse Data
Publicado: 27/4/2025 -
NoWag: Unified Compression for Large Language Models
Publicado: 26/4/2025 -
Optimal Tool Calls in Language Model Reasoning
Publicado: 26/4/2025 -
Data Selection for Empirical Risk Minimization
Publicado: 26/4/2025 -
LoRe: Low-Rank Reward Modeling for Personalized LLMs
Publicado: 26/4/2025 -
ParaPO: Reducing Language Model Verbatim Reproduction
Publicado: 26/4/2025 -
Test-Time RL: Self-Evolving LLMs via Majority Voting Rewards
Publicado: 25/4/2025 -
Tina: Tiny LoRA Reasoning Models
Publicado: 25/4/2025 -
Evaluating large language models in theory of mind tasks
Publicado: 25/4/2025 -
QUEST: Quality Sampling for Machine Translation
Publicado: 24/4/2025 -
Offline Preference Learning via Simulated Trajectory Feedback
Publicado: 24/4/2025 -
Reasoning Elicitation in Language Models via Counterfactual Feedback
Publicado: 24/4/2025 -
Eliciting Human Preferences with Language Models
Publicado: 24/4/2025 -
Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
Publicado: 24/4/2025 -
γ-Bench: Evaluating LLMs in Multi-Agent Games
Publicado: 24/4/2025 -
DRAFT: Self-Driven LLM Tool Mastery via Documentation Refinement
Publicado: 24/4/2025 -
Optimal Prediction Sets for Enhanced Human-AI Accuracy
Publicado: 24/4/2025 -
Self-Correction via Reinforcement Learning for Language Models
Publicado: 24/4/2025 -
Tractable Multi-Agent Reinforcement Learning through Behavioral Economics
Publicado: 24/4/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
