README.md

metadata

title: SkillForge
emoji: 🔨
sdk: docker
pinned: false
base_path: /web

SkillForge — An RL training environment where LLM Agents evolve from "reinventing the wheel" to "building a tool library."

What It Is

An OpenEnv RL environment that trains an agent to discover and reuse parameterized code skills across a sequence of Python DataFrame tasks. The core thesis: an agent that builds a skill library solves the same set of tasks in fewer steps than one that generates from scratch every time.

Core Concept

When solving DataFrame processing tasks, the Agent can choose:

1. Raw Code: Write full code from scratch every time (high token cost)
2. Create Skill: Abstract common operations (e.g., sort, filter) into reusable templates and save to Skill Library
3. Use Skill: Call stored skills (low token cost)

Key Mechanism: Skill Library persists across Episodes. Through training, the Agent discovers that reusing existing skills yields higher rewards than rewriting code.

Key Features

• Persistent Skill Library: JSON-based storage that survives across episodes (simulates "learning to remember")
• Redundancy Detector: Penalizes agents for rewriting existing functionality
• Token Accountant: Tracks computational cost (simulated API expenses)

Tech Stack (OpenEnv)

• Environment: skill_forge (modified from coding_env, executes Python/Pandas code)
• Action Space: raw_code | create_skill | use_skill | finish
• Reward: Task completion (sparse) + Token efficiency (dense) + Skill reuse rate (innovation)
• Training: GRPO (single-agent, stable convergence)