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 )