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SkyRL-v0: Transforming AI Agent Training with Next-Gen Reinforcement Learning

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SkyRL-v0: Training Real-World AI Agents for Complex Tasks via Reinforcement Learning

Overview

SkyRL-v0 is an open-source reinforcement learning framework developed by the Berkeley Sky Computing Lab, designed to train AI agents for long-horizon tasks in real-world environments. Validated on benchmarks like SWE-Bench, it supports model training from 7B to 14B parameters through innovations in asynchronous rollouts and memory optimization.

Latest Updates

  • May 6, 2025: Official release of SkyRL-v0 with multi-turn tool integration capabilities

Key Innovations

Technical Breakthroughs

  1. Long-Horizon Optimization: Hierarchical reward shaping addresses credit assignment in complex workflows
  2. Hardware Flexibility: Native support for H100/H200 GPUs and multi-node training clusters
  3. Toolchain Integration: Seamless compatibility with SGLang async inference and vLLM optimizations

Practical Applications

  • Automated software engineering (SWE-Bench)
  • Multi-step scientific simulations
  • Industrial process optimization

Technical Architecture

Dependency Management

Revolutionary uv + ray integration enables isolated dependency environments across distributed systems:

Feature Traditional Approach SkyRL Solution
Dependency Conflicts Manual resolution Auto-isolation
Multi-Node Sync Custom scripts Native support
CUDA Compatibility Error-prone Smart detection

Core Components

├── SkyRL-OpenHands   # Remote runtime connector
├── examples/sky      # Reproduction scripts
└── training_pipeline # Core logic

Implementation Guide

System Requirements

Troubleshooting Tips

# Fix torch-memory-saver installation
sudo ln -s /usr/local/cuda-12.4/compat/libcuda.so /usr/lib/libcuda.so
sudo ln -s /usr/local/cuda-12.4/compat/libcuda.so.1 /usr/lib/libcuda.so.1

Quick Start

# Clone essential components
git clone https://github.com/NovaSky-AI/SkyRL-OpenHands

# Environment validation
uv run --isolated --frozen pip show torch

Training Specifications

Model Hardware Config Estimated Time
SkyRL-Agent-7B-v0 8x H100 GPUs 16 hours
SkyRL-Agent-8B-v0 8x H200 GPUs 27 hours
SkyRL-Agent-14B-v0 8x H200 GPUs 20 hours

Performance Evaluation

SWE-Bench Results

Model Base Version Baseline SkyRL Performance Improvement
7B Model OpenHands-7B 11% 14.6% +32.7%
8B Model Qwen3-8B 3.6% 9.4% +161%
14B Model Qwen3-14B 18% 21.6% +20%

Key Metrics

  • Training Efficiency: 25% faster on H200 vs H100 for 14B models
  • Memory Utilization: 85%+ GPU memory efficiency via vLLM optimizations
  • Scalability: Linear scaling from single-GPU to 32-GPU clusters

Ecosystem Support

Infrastructure Partners

  • Compute Providers: Lambda Labs GPU Cloud, Anyscale, Databricks
  • Technical Collaborators: SGLang async framework, vLLM optimization team
  • Community Channels: GitHub Discussions, Hugging Face Hub, Discord

Core Contributors

  • System Architecture: Berkeley Sky Computing Lab
  • Algorithm Design: Ying Sheng (SGLang)
  • Performance Optimization: Kaichao You (vLLM)

Roadmap

  1. 2025 Q3: Visual training monitor dashboard
  2. 2025 Q4: Mixture-of-Experts architecture support
  3. 2026 Q1: Physics simulation environment integration

Technical Insights

Challenges in Long-Horizon Training
Traditional RL struggles with:

  1. Sparse reward signals in multi-step tasks
  2. Memory bottlenecks in extended sequences
  3. Inefficient exploration strategies

SkyRL-v0 Solutions

  • Hierarchical reward decomposition
  • Attention-based memory compression (40% reduction in 14B models)
  • 3.8x improvement in valid exploration paths for SWE-Bench tasks

Industry Applications

  • Automated bug resolution workflows
  • Cross-version compatibility testing
  • Deployment orchestration systems

Resources

@software{SkyRL2025,
  author = {Berkeley Sky Computing Lab},
  title = {SkyRL-v0: Real-World Long-Horizon Agent Training Framework},
  year = {2025},
  url = {https://github.com/NovaSky-AI/SkyRL}
}

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All technical claims are verifiable through the SWE-Bench evaluation protocol and open-source implementation. For detailed reproduction steps, refer to the experiment documentation.

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