agent-training 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
Agentic RL 中文零基础教程（33 章）：从概念到 GRPO 实战，含 TRL 最小可跑示例；26–33 章附一套可运行的三方判别模型实证工程（encoder vs LLM-LoRA vs 规则基线）。第 25 章讲清 Jev / TypeSafe System One 与 RL 的能力边界 | Chinese Agentic RL tutorial (33 chapters) + a reproducible discriminative-model benchmark
A curated list of awesome resources about reward construction for AI agents. This repository covers cutting-edge research, and practical guides on defining and collecting rewards to build more intelligent and aligned AI agents.
[NeurIPS 2026] FedAgent: A Library for Decentralized Agent Learning. 🏆 Best Paper Award at the AAAI'26 TrustAgent Workshop, 🏆 Outstanding Paper Award in the AAAI'26 PerFM Workshop.
Official Code for AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration — Learning from Cheap, Optimizing Expensive
Self-Mutating Agent Gym
A curated list of agentic environment synthesis, evolution, quality & scaling — built on three surveys (AEE, Environment Scaling, ACE lens)
A realistic multi-agent diagnostic environment for training and evaluating medical agents.
Turn any real software into a replayable RL environment for training AI agents — deterministic replay, verifiable rewards, TRL & verifiers adapters.
Synthetic environments for training robust tool-use agents via trajectory synthesis
AI 个人记忆训练库：让 AI 记住你的沟通习惯与环境事实，越用越懂你（两级记忆架构，Claude Code 适配）
Implicit is the experience layer for AI agents. For large agent training/evaluation environments, address experiences and materialize required state on demand. Python package: implicit-ai.
14 Principles. 23 Real Corrections. A systematic methodology for training AI agents through correction loops.
Audit synthetic agent-training tasks for environment feasibility, verifier quality, evidence gaps, and batch coverage.
The Vercel for Agent Training - Train production-ready AI agents with 95% tool reliability
AI schooling: experienced agents train newly hatched ones, then examine them cold to graduate them. The coop is the local RAPP neighborhood - several twins (human and AI), one world, no collisions. Pattern dedicated to the public domain.
Latest advances in agentic kernel optimization.
The Flight Simulator for Production AI Agents — Generate high-quality synthetic trajectories for training reliable SRE, DevOps, and infra agents.
A bilingual, buildless, single-file landing page for an agent training-environment platform. No bundler, no framework, no runtime CDN.
Neurochemical behavior training for AI agents — PentaDrive model with 5 drives, 3 phases, MCP server, and structured training modules
Local macOS recorder for voluntary computer-use demonstrations and agent-training datasets. Timestamped inputs, cursor trails, and read-only MCP. Experimental.