Chapter 15: The No-Weights Complement
Understand Reflexion notes and skill accretion as zero-GPU learning, check the idea, then wire a notes file and one skill into the harness.
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Chapter 15: The No-Weights Complement
Objective
Add learning mechanisms that run in parallel with weight updates and cost zero GPU: Reflexion-style notes and skill accretion.
Concept
Reflexion-style notes
After each run the agent writes a lesson to a persistent file; next run reads it. Instant learning, no training.
Skill accretion
When the agent solves something reusable, it writes a script or SKILL.md and keeps it. Experience becomes tooling.
Why this belongs in a fine-tuning course
Fine-tune → competent at the loop. Notes/skills → competent at your repo, this week. Different timescales, no conflict.
This is also the resolution of “don’t bake codebase facts into weights”: notes and skills are the deliberate context side of that split.
Concept check
Q1. What kinds of knowledge should go into notes/skills rather than LoRA weights?
Q2. How do notes and fine-tuning complement rather than replace each other?
Concept answer key — attempt first
Answer key (concept)
Q1
Model answer: Repo-specific, fast-changing facts, local conventions, one-off caveats — things that go stale in weights.
Pass criteria: volatile / repo-specific knowledge
Q2
Model answer: Weights hold durable loop/protocol habits; notes hold current project context — both run together.
Pass criteria: loop vs context timescales
Gate: Notes file in the harness
- After each run (or each failure), append a short lesson to
memory/notes.md(or similar) - Prepend or inject notes into the next system/user context
Pass: two sequential runs; second run’s prompt includes a note written after the first.
Gate: One skill artifact
When a reusable fix appears, write skills/example-SKILL.md or a small script and teach the agent (or yourself) to load it.
Pass: skill file exists; harness or docs reference how it is loaded.
Check your understanding
Q3. Why does this chapter cost “zero GPU” but still improve agent quality?
Q4. If you only fine-tune and never take notes, what class of failures keeps recurring?
Answer key — attempt every question first
Answer key
Q3
Model answer: It changes context/tools the agent sees, not weights — still improves next-run behavior.
Pass criteria: context/tooling not training compute
Q4
Model answer: Project-specific gotchas and repo facts the weights should not memorize.
Pass criteria: recurring local/context failures