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name: research-methodology description: Ethan Perez's tips for empirical alignment research - velocity, experimentation, collaboration

Research Methodology: Tips for Empirical Alignment Research

Based on Ethan Perez's post. Applies to highly experimental LLM alignment research (scalable oversight, adversarial robustness, chain-of-thought faithfulness, process-based oversight, model organisms of misalignment).

Core Principles

1. Hard Work Pays Off

• Run as many experiments as you can, tinker a lot, try lots of stuff
• Often many reasonable-sounding ideas need testing—sometimes the 5th or 20th thing is what works
• "Rich get richer" effect: the more you experiment, the better you get at picking the right experiments
• More things work when you try more stuff → easier to stay motivated

2. Rapid Iteration (Velocity)

• Reduce uncertainty at the fastest possible rate (Jacob Steinhardt)
• Get quick feedback and iterate on ideas rapidly
• With LLMs, you can reduce uncertainty really quickly—even with a single message to GPT-4/Claude
• Can gain 1+ OOMs more information per unit time by derisking ideas in the quickest way possible

3. Avoid the "Swamp"

• The swamp = getting stuck when you really want technique X to work but nothing succeeds
• Solution: high velocity—test as many ideas as possible per unit time until you escape
• For LLM research: you should rarely be stuck in a swamp
  • If stuck → you've likely exhausted low-hanging fruit on that problem/approach
  • There's other low-hanging fruit elsewhere—go pick that instead

4. Low-Hanging Fruit Abundance

• The field is moving so quickly that there's much more low-hanging fruit than almost any other field
• Each new model capability = time to be opportunistic, explore what's now possible
• High velocity is useful for both:
  • Picking low-hanging fruit quickly
  • Getting through swamps when you must solve a particular problem

Collaboration & Communication

Strong Collaborator Qualities

Ethan puts 70% weight on "getting ideas to work quickly" as criteria. Other qualities:

• Receptive to feedback
• Adds emotional energy rather than draining it
• Transparent/communicative about issues faced
• High-trust relationship where various topics can be discussed easily
• Notices and calls out room for improvement in collaboration

Communication Best Practices

• Overcommunicate: bring up issues during meetings or privately—nip problems in the bud
• Close mentorship is maybe the fastest path to become an expert in a domain
• Take agency: organize coworking, discussion groups, standups—whatever helps
• Get feedback from peers, iterate on project plans

Research Workflow

• Have a clear project plan with motivation and research goals
• List all experiments you can think of running
• Think about milestones and deliverables to stay accountable
• Know what tools are available—sharing tooling increases experimental velocity

Paper Writing

• See ethanperez.net/easy-paper-writing-tips/ for ML paper writing tips
• Write short paragraphs composed of short sentences
• Write comprehensive abstracts
• Seek feedback from a naive audience
• Distinguish confirmation from exploration in your writeup

Related Resources

• Tips and Code for Empirical Research Workflows
• Jacob Steinhardt's blog on reducing uncertainty
• Michael Bernstein's slides on velocity