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RewardHarness
Self-evolving agentic reward framework for image-editing evaluation — 47.4% on EditReward-Bench from only 100 preference demos, no reward-model training. arXiv 2605.08703.
11stars
1forks
Python
agenticgeminiimage-editingpreference-learningqwen-vlreward-modelrlhfself-evolvingvlm
Quality signals
ActiveNo testsNo CIThin docsNo licenseNew
Install
Universal installANY TOOL
$ npx skillcity@latest TIGER-AI-Lab/RewardHarnessAuto-detects the skill or MCP server in this repo and installs it for Claude Code, Codex, Cursor, Cline, OpenCode, Gemini CLI, Anti-Gravity, OpenClaw and Hermes.
Requires Node.js 18+ (ships with npx).
Prefer to set it up by hand? Clone the repository:
git clone https://github.com/TIGER-AI-Lab/RewardHarness.git
Loading README…
Ask a simulated agent to run this skill. It loads the real SKILL.md and reasons over your task with NVIDIA.
rewardharness — sandboxAI sandbox
Pick a quick prompt below or type your own — watch the agent execute this skill step by step.
guest@skillcity:~$Try a prompt