Split failure reflections into SKILL_DEFECT (body edit) vs EXECUTION_LAPSE
(protected appendix note that re-emphasizes an existing rule, never edited
by step-level analysts). Toggle: optimizer.use_skill_aware_reflection
(default false; baseline byte-identical when off).
- optimizer/appendix.py: protected APPENDIX region (inject/extract/append
with dedup), mirrors the slow_update protected-field pattern
- optimizer/skill_aware.py: analyst prompt augmentation, appendix_notes
parsing, threshold-gated LLM consolidation, and a process-wide runtime
switch (configure_skill_aware_reflection) set once by the trainer
- gradient/reflect.py: augment error/success analyst prompts at runtime;
None-sentinel kwargs resolve from the global switch, so env adapters
need no per-benchmark wiring (works for all envs, present and future)
- optimizer/skill.py: generalize the protected-region check to
(slow_update, appendix); edits inside any protected region are skipped
- engine/trainer.py: inject appendix at init, flush per-step
EXECUTION_LAPSE notes after the gate settles, optional consolidation
- tests: regression suite incl. toggle-off byte-identical guarantee and
env-independent global-switch resolution (6/6 passing + live smoke)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
