SkillOpt

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Author	SHA1	Message	Date
Yif-Yang	a0b6db59f8	fix(scheduler): preserve existing annealing contract	2026-07-13 16:57:07 +00:00
黄云龙	9f9f87c064	fix(scheduler): use consistent (step-1)/(total_steps-1) endpoint formula LinearScheduler and CosineScheduler now guarantee step() returns max_lr on the first call and min_lr on the total_steps-th call, using the standard endpoint parameterisation t = (step-1)/(total_steps-1). This resolves the inconsistent first-step semantics reported in review.	2026-07-14 00:45:12 +08:00
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

Yif-Yang

a0b6db59f8

fix(scheduler): preserve existing annealing contract

2026-07-13 16:57:07 +00:00

黄云龙

9f9f87c064

fix(scheduler): use consistent (step-1)/(total_steps-1) endpoint formula

LinearScheduler and CosineScheduler now guarantee step() returns max_lr
on the first call and min_lr on the total_steps-th call, using the
standard endpoint parameterisation t = (step-1)/(total_steps-1).

This resolves the inconsistent first-step semantics reported in review.

2026-07-14 00:45:12 +08:00

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