# Echo
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Research paper: [published](https://arxiv.org/pdf/2508.05387)
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Models: [open-sourced](https://huggingface.co/GradientResearch/models)
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As AI systems grow in scale and generality, RL has become a critical layer for alignment. However, most co-located RL frameworks face a deadlock: inference interrupts training, training delays inference.
Echo is Gradient’s distributed RL framework, running on everyday consumer devices.
## Dual Swarm Architecture
Echo breaks the cycle by decoupling them into two independent swarms:
* Inference on distributed devices (via [Parallax](/the-open-intelligence-stack/parallax.md))
* Training on high-performance clusters
## Echo Modes
We have open-sourced several early explorations with Echo, including:
* A 7B math model beating Qwen2.5–32B
* A 30B Sokoban model outperforming DeepSeek-R1 & GPT-OSS-120B
* A 32B LoRA logic model ≥99% accuracy
See all models in our [Hugging Face Collection](https://huggingface.co/GradientResearch/models).
## Learn More
Read the [blog](https://gradient.network/blog/echo-distributed-reinforcement-learning).\
Read the full [research paper](https://arxiv.org/pdf/2508.05387).
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