3 Commits

Author	SHA1	Message	Date
Shunsuke	98d0430bee	refactor: make EnvAdapter.reflect a shared default (fixes dropped reflect kwargs) ... All six adapters duplicated an identical reflect() that delegates to run_minibatch_reflect. The copies had drifted: OfficeQA/DocVQA silently dropped meta_skill_context and ALFWorld dropped update_mode, so those analysts ran without inputs every other benchmark receives (active under the default use_meta_skill: true). Move the delegation into EnvAdapter.reflect as one default that forwards all kwargs uniformly, and delete the six overrides. reflect is no longer abstract — adapters inherit it and override only for custom logic. Net -225 lines. Behavior change: OfficeQA/DocVQA/ALFWorld reflect now receive the kwargs they previously dropped; the three already-correct benchmarks are unaffected. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-15 09:06:00 +00:00
Cuzyoung	4a1b984d87	refactor: rename teacher/student to optimizer/target, remove best skills, fix slow update ... - Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts - CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model - Remove best_skill files, keep only initial skills - Fix slow update gate (force write into skill) - Fix SLOW_UPDATE marker stripping - Remove deep_reflect and meta_reflect mechanisms - Update .env.example with export prefix and azure_cli docs - Add endpoint empty validation in azure_openai.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>	2026-05-24 19:15:10 +00:00
CharlesYang030	244e346b83	SkillOpt v0.1.0: initial release ... - 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	2026-05-21 17:22:04 +00:00