Gear开源框架探索递归式AI自我改进
AI 总结
Gear开发者推出一个受元学习启发的开源框架：任务智能体生成执行轨迹，元智能体再利用这些轨迹和评估反馈改进提示词、工具、工作流及上下文管理。该框架将搜索决策与执行、评估相分离，并提供一个带共享失败诊断和分阶段评估的可运行GEPA变体。其目标是让GEPA、RRSI和Harness²等方法更易于实现和比较。开发者报告称，在GPT 5.6 Luna采用中等推理强度时，Gear优化配置的任务通过率有所提高，但没有提供独立测量数据。
深度见解
Gear提供了一个开源实现层，旨在让递归式自我改进方法更便于开发和比较。目前的性能结果仅由开发者自行报告，尚未经过独立验证。
| After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search: That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates: At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. Code · Paper submitted by /u/Pale-Training-6944 [link] [comments] |
#Gear#GEPA#RRSI#Harness²#GPT 5.6 Luna#Astra#Codex#AutomationBench#Terminal-Bench 2.1