RL Systems Knowledge Map
This is a framework to fill in, not a claim about completed projects or proficiency.
| Dimension | Questions to answer |
|---|---|
| Objective and algorithm | What is optimized? How do advantage estimation, clipping, and KL constraints affect learning? |
| Data flow | How do prompts, rollouts, rewards, and training batches move through the system? |
| Execution | How are training and inference resources allocated? What changes between synchronous and asynchronous execution? |
| Performance | How do you measure throughput, GPU utilization, tails, memory, and communication? |
| Stability | How do you detect stale policy data, version mismatch, numerical failures, and reward hacking? |
| Recovery | What state is needed for checkpoints, retries, deduplication, and reproducibility? |
| Evaluation | How do you distinguish reward gains, generalization, and evaluation contamination? |
Answer template
Definition → mathematical objective or data flow → system constraints → failure modes → measurable evidence.
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