How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Meta during technical interviews.

Architect an asynchronous RL post-training system

Quick 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:

Architecture and Components
- Actors/generators, reward inference, learners, replay/buffers, and orchestrators.
- Explicit separation of serving and training clusters.
Dataflow and Queues
- Logging, topics/queues, batching, backpressure, and idempotency.
- Online/offline feedback ingestion.
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.
Safety and Compliance
- Prompt/content filters, rate limits, canary gating.
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.

Quick Overview

Context

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:

Architecture and Components

Actors/generators, reward inference, learners, replay/buffers, and orchestrators.
Explicit separation of serving and training clusters.

Dataflow and Queues

Logging, topics/queues, batching, backpressure, and idempotency.
Online/offline feedback ingestion.

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.

Safety and Compliance

Prompt/content filters, rate limits, canary gating.

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.

Architect an asynchronous RL post-training system

Quick Overview

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

Context

Requirements

Solution

Comments (0)

Architect an asynchronous RL post-training system

Quick Overview

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

Context

Requirements

Solution

Comments (0)