Scaling Reinforcement Learning
summary
Describes a production recipe for RLHF at scale: training on Kubernetes multi-GPU clusters (4–8 GPUs per pod) using DeepSpeed and Ray Train with NCCL/NVLink for low‑latency all‑reduce, checkpointed runs that resume after weeks of compute, and gradient/data/model parallelism choices. Calls out concrete failure modes—network bandwidth, reward hacking, uneven GPU utilization—and practical fixes like checkpoint-based failover and tight GPU monitoring and load balancing.
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chapters (10)
- 0:00Introduction to Reinforcement Learning
- 0:32Agenda Overview
- 1:15Foundations of Distributed Reinforcement Learning
- 2:06Challenges in Distributed Learning
- 2:48Human Feedback and Supervised Fine-Tuning
- 4:10Distributed Implementation Strategies
- 6:17Cloud Native Architectures and Frameworks
- 9:19Production Deployment Strategies
- 14:21Emerging Trends and Future Directions
- 16:59Conclusion and Q&A
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