RL post-training on 14 Macs across 4 countries
Summary
A technical note on performing RL post-training across 14 Macs distributed in 4 countries, highlighting distributed compute for reinforcement learning.
Similar Articles
Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback
This paper studies the compute allocation problem in RL post-training for foundation models, proposing a FLOP-accounting framework for GRPO post-training. It finds conditional allocation frontiers depending on model size, budget, and reward system, and introduces RACE as a diagnostic protocol.
@charles_irl: Proper post-training RL, deployed broadly, is a key step towards a future where software systems quietly improve themse…
Modal announces an open-source library for reinforcement learning on its platform, addressing infrastructure challenges in post-training RL with scalable deployment.
@mronge: I run 4 computers each running a different AI agent. Here's a short tour of how I'm splitting up work between them in m…
A user shares a tour of their setup running four different AI agents on separate computers to split up work.
Building Fast & Accurate Agents with Prime-RL Post Training (22 minute read)
Ramp presents a case study on using reinforcement learning post-training to build Fast Ask, a specialized spreadsheet retrieval agent that improves accuracy and reduces latency compared to general-purpose models.
Macs for Local LLM and Openclaw - What I wish I had known.....
A user shares their experience running local LLMs on Mac, noting that prompt processing is slow for AI agents compared to Nvidia GPUs, and recommends cloud models like Deepseek unless privacy is a concern.