How to do Distributed RL Training for LLM? feat. Eric Yang from Gradient
Key Takeaways
Distributed RL training for LLMs with Eric Yang from Gradient
Original Description
Currently most of the post-training of large language models are done via reinforcement learning in a centralized cluster of GPUs.
Interestingly about 80% of the wall-clock time is spent on rollout generation which can are kind of simple in the sense that they are inference-only forward passes. This means that quite a lot of compute time is wasted.
In this video, I’m going to present you a method to better use centralized data center GPUs for post-training by offloading these roll-out generations to a distributed fleet of workers all across the world.
Interesting Articles and Links:
📌 Echo 1: https://arxiv.org/abs/2508.05387v1
📌 Echo 2: https://arxiv.org/abs/2602.02192
📌 blog about the framework: https://gradient.network/blog/echo-2-unlocking-the-second-scaling-law
Also for beginners:
📌 learn to code from full-stack to AI with Scrimba https://scrimba.com/?via=yacineMahdid (extra 20% off pro with my link, great resource, I love the team)
# Table of Content
- current state of RL training 0:00
- overview of echo methodology: 1:46
- Echo 1 breakdown: 4:45
- Echo 2 breakdown: 7:44
- Eric Yang Interview: 19:17
- what does General Collective Intelligence means: 26:26
- how does the gradient stack fit into that vision: 33:10
- intuition behind the p2p broadcast system: 38:14
- why Echo 2 focus is primarily on distributed roll-out: 41:47
- was there any specific research direction that motivated echo2: 45:15
- extending distributed training beyond LLM finetuning?: 47:20
- how tightly coupled with parallax the system is? 50:09
- stance on off policy RL finetuning?: 55:37
- how does long roll-out fit in this paradigm?: 59:55
- how would these result hold up with more parameters?: 1:02:41
- how to manage the staleness knob for user? 1:04:50
- what does day 1 for a startup training with echo2 a 7b looking like: 1:06:53
- what are the next step in this research direction?: 1:08:20
- conclusion: 1:09:54
About Echo:
ECHO-2 is a distributed reinforcement learning framew
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