⚡AgentSkills
🧮 ML Research Engineering · Inference Optimization

Read scaling-law claims like a practitioner

Compute budgets, data-wall caveats, and extrapolation ranges separating signal from slide-ware.

advanced~35 minResearch EngineersML ScientistsPhD Researchers

Steps

  1. 1Check the compute range fitted — extrapolation beyond it is faith, not math
  2. 2Ask what's held constant: data quality? architecture? tokenizer?
  3. 3Convert claimed multipliers into YOUR training-budget currency honestly
  4. 4Note whether inference cost was counted; many 'efficient' models aren't
  5. 5Demand error bars or seed variance on fitted exponents
  6. 6Update decisions only when curves cross within your reachable budget

Common Pitfalls

  • ▲Chinchilla-optimal quoted for inference-dominated deployments
  • ▲Data-quality assumptions inherited from web-scrape era

Commands

Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill inference-optimization-scaling-law-interpretation
Install globally
$ npx skills add aniruddhaadak80/skills --skill inference-optimization-scaling-law-interpretation -g

Tags

#scaling-laws#strategy#analysis#ml-research#inference-optimization

Related skills

Tune speculative decoding without regressing qualityadvanced

Drafter selection, acceptance-rate monitoring, and batch-interaction effects in production serving.

🧮 ML Research Engineering·~45m
Open →