Podcast

The podcast where we breakdown the recent AI papers and explain them in simple terms for you to understand.

Arxiv paper – Self-Improving Robust Preference Optimization – AI Breakdown

In this episode, we discuss Self-Improving Robust Preference Optimization by Eugene Choi, Arash Ahmadian, Matthieu Geist, Oilvier Pietquin, Mohammad Gheshlaghi Azar. The paper introduces Self-Improving Robust Preference Optimization (SRPO), an offline RLHF framework that enables models to self-improve and generalize across tasks by jointly optimizing a self-improvement and generative policy through a min-max objective. SRPO reformulates this objective into a non-adversarial offline loss that can be efficiently optimized using supervised learning. Experiments show SRPO significantly outperforms existing methods like DPO and IPO on benchmarks such as XSum and Arena-Hard, achieving higher win rates against human and AI baselines.

1. Arxiv paper – Self-Improving Robust Preference Optimization 05:42
2. Arxiv paper – LLM Post-Training: A Deep Dive into Reasoning Large Language Models 04:59
3. Arxiv paper – Welcome to the Era of Experience 07:17
4. Arxiv paper – MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation 06:19
5. Arxiv paper – InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models 06:13

News

• Arxiv paper – Self-Improving Robust Preference Optimization
  In this episode, we discuss Self-Improving Robust Preference Optimization by Eugene Choi, Arash Ahmadian, Matthieu Geist, Oilvier Pietquin, Mohammad Gheshlaghi… Read more: Arxiv paper – Self-Improving Robust Preference Optimization
• Arxiv paper – LLM Post-Training: A Deep Dive into Reasoning Large Language Models
  In this episode, we discuss LLM Post-Training: A Deep Dive into Reasoning Large Language Models by Komal Kumar, Tajamul Ashraf,… Read more: Arxiv paper – LLM Post-Training: A Deep Dive into Reasoning Large Language Models
• Arxiv paper – Welcome to the Era of Experience
  In this episode, we discuss Welcome to the Era of Experience by David Silver, Richard S. Sutton. The paper discusses… Read more: Arxiv paper – Welcome to the Era of Experience
• Arxiv paper – MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation
  In this episode, we discuss MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation by Sihyun Yu, Meera Hahn, Dan… Read more: Arxiv paper – MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation
• Arxiv paper – InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
  In this episode, we discuss InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models by The authors of… Read more: Arxiv paper – InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models