Architect an asynchronous RL post-training system
Overview
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
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:
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Architecture and Components
- Actors/generators, reward inference, learners, replay/buffers, and orchestrators.
- Explicit separation of serving and training clusters.
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Dataflow and Queues
- Logging, topics/queues, batching, backpressure, and idempotency.
- Online/offline feedback ingestion.
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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.
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Safety and Compliance
- Prompt/content filters, rate limits, canary gating.
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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.
Solution
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