Reinforcement Learning: The Future Tense of Intelligence

Reinforcement learning (RL) has repeatedly proven its ability to drive discovery and performance in highly structured domains throughout the modern history of AI. From AlphaGo defeating human champions, to ChatGPT learning to align with human intent, and Tesla’s Autopilot adapting to real-world complexity, RL has emerged as a core mechanism for enabling self-evolving intelligence.

This progression unfolds across three interlinked stages. Pre-training defines a model’s raw potential by providing a broad base of knowledge. Inference then puts that knowledge into action, generating outputs in dynamic, real-world contexts. RL closes the loop by evaluating those outputs and applying feedback to further optimize model performance in specific problem settings. Through this cyclical process, models do not merely perform—they adapt and improve over time.

As AI systems grow in scale and generality, RL has become a critical layer for alignment. Techniques like RLHF (RL from Human Feedback) and DPO (Direct Preference Optimization) are increasingly central to ensuring that model behavior aligns with human goals and values.

The Bottleneck to Scale

RL pipelines follow a natural multi-program, multi-data (MPMD) pattern. They consist of two interdependent phases: trajectory sampling (inference) and policy optimization (training), each with distinct computational characteristics. Sampling is latency-sensitive and irregular, while optimization is batch-oriented and demands high throughput.

Despite this, most RL frameworks still adopt a single-program, multi-data (SPMD) infrastructure model. They co-locate both phases on the same GPU cluster and treat them as components of a unified loop. This tight coupling leads to frequent context switching between workloads that are fundamentally at odds. Sampling interrupts training, and training delays inference, creating under-utilization, slower iterations, and rising costs.

This structural inefficiency imposes a ceiling on scalability, keeping RL chained to centralized and homogeneous clusters.

Echo offers a clean break. It is a distributed RL framework that removes these limitations by decoupling inference and training and running them asynchronously on distributed, specialized swarms.

How Echo Works

Dual-Swarm Architecture

Echo introduces a clean architectural separation. Rather than co-locating all RL workloads on a single cluster, it distributes inference and training across two dedicated, heterogeneous swarms. Each swarm is optimized for its specific computational profile and can scale independently.

Inference Swarm–Optimized for low-latency trajectory generation. Echo leverages the Inference Swarm from Parallax—Gradient’s distributed inference engine. Running across globally distributed, heterogeneous consumer-grade hardware, including RTX 40/50-series GPUs and Apple Silicon (M-series), it enables scalable rollout generation and supports a wide range of AI workloads on everyday devices.
Training Swarm–Optimized for high-throughput gradient updates. It operates on datacenter-grade accelerators such as A100 and H100 clusters, ensuring efficient and stable policy optimization at scale.

This dual-swarm design ensures each workload runs where it performs best, optimizing hardware utilization without compromising the statistical efficiency of standard RL algorithms.

Principled Synchronization

Decoupling inference and training introduces a key challenge: maintaining policy freshness. Without coordination, inference peers may generate rollouts using outdated policies, which can degrade training quality or cause divergence.

Echo addresses this challenge with two lightweight, tunable synchronization protocols that preserve statistical integrity without relying on expensive interconnects such as InfiniBand or NVLink.

Sequential Mode (Accuracy-Centric): The training swarm requests trajectories on demand. Before responding, inference peers validate and refresh their local policy weights. This mirrors the behavior of a single-process RL loop, ensuring minimal bias and maximum statistical accuracy.
Asynchronous Mode (Efficiency-Centric): Inference peers continuously stream version-tagged rollouts into a replay buffer. The training swarm consumes data at its own pace. A lightweight coordinator monitors version drift and initiates resynchronization when necessary. This mode prioritizes throughput and utilization by overlapping compute and communication.

Together, these two modes give users fine-grained control over the balance between accuracy and efficiency, making Echo adaptable to a wide range of hardware configurations and deployment environments.

Leveraging a State-of-the-Art Stack

Echo is built on top of Gradient’s distributed AI infrastructure, integrating specialized protocols that enable high-performance RL across heterogeneous environments.

Parallax for Distributed Inference: Echo’s inference swarm runs on Parallax, Gradient’s distributed inference engine purpose-built for heterogeneous, consumer-grade hardware. Parallax unifies globally dispersed devices into a high-throughput sampler using KV-cache fusion and adaptive batching. This architecture enables efficient, large-scale rollout generation without relying on centralized compute.
Enhanced verl for Efficient Training: The training swarm extends the open-source verl stack and supports standard algorithms such as PPO, GRPO, and DPO. It also integrates production-grade Low-Rank Adaptation (LoRA) to significantly reduce checkpoint sizes and minimize synchronization costs between distributed peers.

Benchmarking

The central research question behind Echo was simple:Can a decoupled RL architecture, running on heterogeneous and globally distributed devices, match the training efficiency and final performance of a traditional, tightly coupled setup?

To evaluate this, we benchmarked Echo against a standard co-located verl baseline across three diverse tasks:

Sokoban (long-term planning)
Mathematical Problem Solving
Knights & Knaves (logical reasoning)

Experimental Setup

Baseline (verl): Training and inference co-located on 8× A100 80GB GPUs
Echo: Training on a swarm of 4× A100 GPUs; inference fully offloaded to a distributed Parallax swarm of 3× RTX 5090 GPUs and 3× Mac M4 Pro devices.

Results

Despite cutting datacenter GPU usage in half and moving inference to consumer-grade hardware, Echo matched the baseline across all tasks in both convergence speed and final reward. This validates that high-performance RL can be achieved on distributed, heterogeneous infrastructure without compromising efficiency, stability, or correctness.

Echo Models: Smaller, Smarter

As early explorations of Echo’s capabilities, we trained a handful of models with the GRPO algorithm. While these runs were limited in scope, the results were quite promising.

Qwen2.5-7B-ECHO-MATH-GRPO: A 7B model that showed consistent improvements over the much larger Qwen2.5-32B baseline across six major math reasoning benchmarks, averaging a +12% gain.
Qwen3-30B-ECHO-Sokoban-GRPO: This model reached 82.2% on the Sokoban planning task, surpassing leading open-source models like DeepSeek-R1 (75.8%) and GPT-OSS-120B (79.7%).
Qwen3-32B-LoRA-ECHO-KK-GRPO: Using Echo’s LoRA-optimized training swarm, the model reached near-perfect (≥0.99) accuracy on all levels of the Knights & Knaves benchmark, outperforming models such as O3-mini-high and DeepSeek-R1.
Qwen3-4B-ECHO-Sokoban-GRPO: A lightweight 4B agent that achieved 34.0% on Sokoban, improving +13% over the base model and highlighting Echo’s efficiency gains at the small-model scale.

View all models on our Hugging Face Collection.

These experiments suggest that architectural efficiency from distributed RL can translate into meaningful performance gains, even without massive model size. They underscore the potential of Echo to make RL more scalable, adaptable, and efficient across a range of model sizes.

Building Collective Intelligence

Echo is our attempt to efficiently coordinate thousands of parallel computations, enabling accurate modeling of dynamic, modern software agents at scale, while leveraging idle, heterogeneous consumer devices.

It marks a pivotal step in scaling RL across distributed infrastructure by decoupling inference from training and allowing each to scale independently. This architecture offers a flexible, cost-efficient alternative to centralized systems, laying the foundation for a permissionless RL bedrock that supports a wide range of environments and problem settings.

Looking ahead, we aim to further reduce synchronization overhead through adaptive update schedules and compression techniques, making large-scale RL increasingly accessible to everyday devices and contributors worldwide.

Together with Lattica (peer-to-peer data communication) and Parallax (distributed inference), Echo forms another core pillar of Gradient’s distributed AI base stack—the foundation for building self-evolving intelligence that is powered and owned by all.

Read the full technical paper on arXiv