Hugging Face Trending Papers

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Stay ahead in AI with Hugging Face Trending Papers — your daily digest of trending ai research. Hosts break down the most talked-about papers in machine learning, LLMs, generative AI, and robotics in just few minutes. Clear, conversational insights on problems, methods, benchmarks, and real-world impact — no jargon overload. Perfect for researchers, engineers, students, and AI enthusiasts.

  1. Episode 9: Boosting AI Problem Solving: Tiny Networks and Early Experience Learning

    10/10/2025

    Episode 9: Boosting AI Problem Solving: Tiny Networks and Early Experience Learning

    In this episode of Hugging Face Trending Papers, we discuss three exciting AI research papers: "Less is More: Recursive Reasoning with Tiny Networks", "Agent Learning via Early Experience", and "Paper2Video: Automatic Video Generation from Scientific Papers". ## Papers Discussed1. **[Less is More: Recursive Reasoning with Tiny Networks](https://arxiv.org/pdf/2510.04871)**: This paper introduces a Tiny Recursive Model that significantly improves accuracy on hard question-answer problems, using a simpler recursive reasoning approach and beating Large Language Models on complex tasks. 2. **[Agent Learning via Early Experience](https://arxiv.org/pdf/2510.08558)**: This research paper presents a new paradigm called "early experience", where AI agents learn from their own actions. The approach improved effectiveness and out-of-domain generalization in diverse environments.3. **[Paper2Video: Automatic Video Generation from Scientific Papers](https://arxiv.org/pdf/2510.05096)**: This paper presents Paper2Video, a multi-agent framework designed to automate the labor-intensive process of generating academic presentation videos from scientific papers. ## Episode Links- [Paper 1: Less is More: Recursive Reasoning with Tiny Networks](https://arxiv.org/pdf/2510.04871)- [Paper 2: Agent Learning via Early Experience](https://arxiv.org/pdf/2510.08558)- [Paper 3: Paper2Video: Automatic Video Generation from Scientific Papers](https://arxiv.org/pdf/2510.05096)

    5 min
  2. Episode 8: Boosting AI Efficiency: Code Compression, Video Generation, and Experience-based Reasoning

    03/10/2025

    Episode 8: Boosting AI Efficiency: Code Compression, Video Generation, and Experience-based Reasoning

    In this episode, we discuss three trending AI research papers. We delve into the challenges and solutions related to code language models, video generation, and reinforcement learning. Key Points Discussed#LongCodeZip: Compress Long Context for Code Language Models- LongCodeZip is a novel framework for compressing code for Large Language Models (LLMs)- It addresses the issue of high API costs and generation latency associated with processing long inputs in codebases- The framework uses a dual-stage compression strategy, enabling it to preserve essential information while reducing context size- Evaluations show that LongCodeZip consistently outperforms baseline methods- This research could improve the efficiency and capability of code intelligence applications #Self-Forcing++: Towards Minute-Scale High-Quality Video Generation- The paper addresses the computational cost of generating long videos with diffusion models- It proposes an approach that uses teacher models to guide student models through sampled segments from self-generated long videos- This method allows for video length scaling up to 20× beyond the teacher's capability- The authors manage to generate videos up to 4 minutes and 15 seconds long, substantially outperforming baseline methods #EXGRPO: Learning to Reason from Experience- The paper investigates what makes a reasoning experience valuable in the context of Reinforcement Learning from Verifiable Rewards (RLVR)- The authors propose a framework that organizes and prioritizes valuable experiences- The approach aims to balance exploration with experience exploitation for efficient and scalable RLVR ### Links to Papers- [ LongCodeZip: Compress Long Context for Code Language Models](https://arxiv.org/pdf/2510.00446 )- [ Self-Forcing++: Towards Minute-Scale High-Quality Video Generation](https://arxiv.org/pdf/2510.02283 )- [EXGRPO: Learning to Reason from Experience](https://arxiv.org/pdf/2510.02245 )

    4 min

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Stay ahead in AI with Hugging Face Trending Papers — your daily digest of trending ai research. Hosts break down the most talked-about papers in machine learning, LLMs, generative AI, and robotics in just few minutes. Clear, conversational insights on problems, methods, benchmarks, and real-world impact — no jargon overload. Perfect for researchers, engineers, students, and AI enthusiasts.