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ai deep-dive 認識 AI 模型

Three RL Post-Training Playbooks: How Ornith, Nous Research, and MiniMax Built Dark Horse Models

Three non-big-lab teams used different RL post-training strategies to produce benchmark dark horses in 2026: Ornith's self-improvement loop (GRPO), Nous Research's DataForge + Atropos execution-reward RL, and MiniMax's massive-scale RL across 200K real environments. Different strengths, but one shared proof point: post-training RL matters more than pretraining scale.

#reinforcement-learning#grpo#post-training#self-improvement#open-source#agentic-coding#fine-tuning
ai guide Hermes Agent Documentation Guide

Memory and Skills in Hermes Agent: A System That Rewrites Itself, and Where You Can Intervene

Hermes memory has hard caps: 2,200 characters for MEMORY.md and 1,375 for USER.md, and an over-limit write returns an error instead of auto-compacting, forcing the agent to make room itself. Skills are maintained by a curator that runs every 7 days after 2 hours of idle, marks skills stale at 30 days and archives at 90 — but never deletes. The switches actually worth flipping are `memory.write_approval` and `skills.write_approval`, which stage the background self-improvement writes for review.

#hermes-agent#memory#skills#self-improvement#agentskills#curator