Approach: Walk through problem space, architecture decisions, and specific scaling bottlenecks identified and solved.

Considerations: Balance compute efficiency, model performance, and latency trade-offs. Discuss monitoring and rollback strategies.

Key Points: Include specific technologies (frameworks, optimizations), metrics improved (throughput, latency), and validation approach.

Approach: Isolate regression to specific components, then apply systematic debugging with profiling tools and hypothesis testing.

Considerations: Non-determinism in distributed systems, hardware variance, and experiment cost. Prioritize high-impact changes.

Key Points: Use profilers, benchmarks, and version control to pinpoint changes. Correlate with throughput, memory, convergence metrics.

Approach: Summarize core contribution and novelty, then propose concrete extensions addressing limitations or enabling new applications.

Considerations: Prioritize extensions aligned with OpenAI's mission on capabilities and safety. Ensure feasibility within compute and measurement constraints.

Key Points: Cite specific sections, discuss reproducibility and implementations, explain experimental validation approach.