System Design: Asynchronous RLHF/RLAIF Post-Training for a Production Chat LLM

Context

You operate a chat LLM that already serves real user traffic. You want to introduce an asynchronous reinforcement learning-based post-training loop (e.g., RLHF or RLAIF) that safely and incrementally improves the model using online and offline feedback, without compromising uptime, quality, or cost predictability.

Assume you have:

• A base SFT model already deployed to a serving cluster.
• Separate training capacity you can provision.
• Access to human raters and/or AI feedback for preferences.

Requirements

Design an end-to-end, asynchronous system that covers:

1. Architecture and Components
   • Actors/generators, reward inference, learners, replay/buffers, and orchestrators.
   • Explicit separation of serving and training clusters.
2. Dataflow and Queues
   • Logging, topics/queues, batching, backpressure, and idempotency.
   • Online/offline feedback ingestion.
3. Learning Details
   • Off-policy corrections (e.g., importance sampling, V-trace) when applicable.
   • KL control to a base/reference model.
   • Credit assignment for delayed/sparse rewards over multi-turn dialogs.
   • Prevention of reward hacking.
4. Safety and Compliance
   • Prompt/content filters, rate limits, canary gating.
5. Deployment and Operations
   • Versioning, canary and phased rollouts, rollback strategy.
   • Monitoring for stability (reward drift, diversity, response quality).
   • Cost predictability under asynchronous feedback and load spikes.

Describe concrete design choices, trade-offs, and failure modes. Include diagrams-in-words as needed.