SkillOpt

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Author	SHA1	Message	Date
Ziiii	0c30b778fb	Preserve continuous rollout hard scores Rollout hard scores can be continuous when smoothed rewards are used. Converting the field through int() turned values like 0.75 into 0, which loses signal before scoring and serialization. Constraint: Keep the existing RolloutResult dictionary shape unchanged. Rejected: Clamp hard scores to 0 or 1 \| contradicts existing continuous-score support in compute_score and sleep replay types. Confidence: high Scope-risk: narrow Reversibility: clean Directive: Treat hard as numeric reward data, not only a binary label. Tested: uv run --with pytest pytest tests/test_types.py tests/test_scoring.py -q Tested: uv run --with ruff ruff check skillopt/types.py tests/test_types.py Not-tested: Full benchmark rollouts.	2026-07-06 15:42:07 +08:00
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

Author

SHA1

Message

Date

Ziiii

0c30b778fb

Preserve continuous rollout hard scores

Rollout hard scores can be continuous when smoothed rewards are used. Converting the field through int() turned values like 0.75 into 0, which loses signal before scoring and serialization.

Constraint: Keep the existing RolloutResult dictionary shape unchanged.

Rejected: Clamp hard scores to 0 or 1 | contradicts existing continuous-score support in compute_score and sleep replay types.

Confidence: high

Scope-risk: narrow

Reversibility: clean

Directive: Treat hard as numeric reward data, not only a binary label.

Tested: uv run --with pytest pytest tests/test_types.py tests/test_scoring.py -q

Tested: uv run --with ruff ruff check skillopt/types.py tests/test_types.py

Not-tested: Full benchmark rollouts.

2026-07-06 15:42:07 +08:00

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

3 Commits