

TrajectoryRL
19 posts

@TrajectoryRL
Reinforcement Learning as a Service for optimizing agent trajectories powered by Bittensor.






We’re launching Season 1: Self-Learning is live 🚀 Introducing trajrl-bench: github.com/trajectoryRL/t… An open benchmark for AI agent harness + skills. Each miner submission is executed 4 times, with results aggregated into a growth-quality score — used to rank and select winners. Key setup: – Hermes as default (expanding to Claude Code, OpenClaw, etc.) – Sandbox only (LLM + mock services, no internet) – SKILL.md as the unified interface – Only submissions from the past 48h are evaluated We’ll keep adding new scenarios to improve signal and avoid overfitting. Goal: Discover skills that outperform existing self-improving agents clawhub.ai/pskoett/self-i… This marks our first step toward a fully automated research and skill production flywheel. There’s much more to explore — let’s build.


The more I study @TrajectoryRL, the more excited I get. It’s building the infrastructure to continuously discover better agent behavior. New scenarios. New evaluations. New skills. Subnet 11’s output could become one of the most valuable assets in the agent economy. $tao





