Breaking Down RL2: Why We Built a Ray-Less RL Framework for AI Agents

After working with existing RL frameworks, we noticed three persistent problems that motivated RL2's development:

1. The Heavyweight Framework Problem
Most production RL systems (like ByteDance's veRL) require:

• Complex infrastructure dependencies
• Significant engineering overhead
• Deep integration with proprietary systems

2. The Reasoning Gap in AI Agents
Current tools (Auto-GPT, AgentGPT, etc.) demonstrate:

• No memory between tasks
• Static decision policies
• Zero learning capability

3. The Prototyping Bottleneck
Researchers and indie developers need:

• Quick iteration cycles
• Minimal setup requirements
• Clear debugging paths

How RL2 Addresses These
Our solution provides:
✅ True modularity (swap components without breaking core)
✅ Distributed training via torchrun (no Ray dependency)
✅ Sub-1000 LOC core for easy understanding

Example Use Case
In our B2B procurement agents, RL2 enables:

• Adaptive negotiation strategies
• Context-aware decision making
• Continuous performance improvement

Let's Discuss
For those working with RL/AI agents:

• What's been your biggest framework frustration?
• How important is simplicity vs features in your work?
• Would a minimalist approach like this help your projects?

Full technical details in our blog post - we'd appreciate any feedback from the dev community.