Quality shows N/A— there isn't enough public data about this tool yet to score it fairly. It's a smaller or newer tool; we'll score it once more reviews and coverage appear online.
What it is
Overview
Miles addresses the challenge of training large-scale reinforcement learning models for agentic workloads, where traditional frameworks struggle with the complexity of multi-turn interactions, tool usage, and frontier model integration. AI researchers and enterprises previously faced engineering bottlenecks when scaling RL training beyond basic scenarios, particularly when working with newly released frontier models that lack established training recipes.
At a glance
Usability & Quality overview
Best for
- large-scale LLM/VLM post-training
- teams building RL training infrastructure
- PyTorch-native frontier-model experimentation
Watch out for
- No verified independent user discussions were found in the retrieved evidence.
- Real-world day-to-day reliability remains unconfirmed.
Real product, not a wrapperIndependent product
PricingFree
Individual plan details haven't been verified yet — they'll appear here on the next data refresh.
Capabilities
Key features
Agent Builder
Builds autonomous AI agents that plan and execute multi-step tasks for you
Developer Tools
Provides utilities that help programmers build, test, and ship software faster
Questions
Frequently asked
What is Miles?
Miles is an open-source reinforcement learning framework designed for training large-scale AI models with native agentic support. It specializes in handling complex multi-turn interactions, tool usage, and frontier model integration at enterprise scale. The platform provides day-zero compatibility with newly released frontier models and supports distributed training through backends like Megatron-LM and FSDP2.
Is Miles free to use?
Yes, Miles is available as an open-source framework through GitHub, allowing organizations to use it without licensing costs. Since it's open-source, users maintain full control over their training data, model checkpoints, and deployment infrastructure.
What makes Miles different from other RL training frameworks?
Miles distinguishes itself through day-zero support for newly released frontier models, eliminating the typical lag time between model release and training framework compatibility. It features native agentic support with its Token-in-Token-Out (TITO) system that preserves exact tokens and metadata during rollout, plus asynchronous architecture that allows concurrent rollout generation and optimizer steps.
Which frontier models does Miles support?
Miles provides verified support for frontier model families including DeepSeek, Kimi, GLM, Qwen, and Nemotron. It works across dense, mixture-of-experts, and multimodal architectures with day-zero compatibility for newly released models.
What hardware does Miles work with?
Miles supports NVIDIA Blackwell, Hopper, and Ampere architectures, plus AMD Instinct via ROCm. It also supports low-precision training formats including FP8, MXFP8, and NVFP4 for efficient resource utilization.
What training algorithms does Miles include?
Miles encompasses supervised fine-tuning (SFT), reinforcement learning algorithms including GRPO, GSPO, PPO, and REINFORCE++, plus on-policy distillation. All training methods are available in both full-parameter and LoRA configurations.
Can Miles integrate with existing agent frameworks?
Yes, Miles integrates with existing agent frameworks like Harbor, HUD, NeMo Gym, OpenEnv, and Verifiers. It also provides sandbox support through Daytona, E2B, Modal, and AgentENV for complete agent workflow management.
How does Miles handle multi-turn agentic interactions?
Miles uses its Token-in-Token-Out (TITO) system to manage trajectory data while preserving exact tokens and metadata during rollout. It supports complete agent workflows including model responses, tool calls, observations, and environment interactions with features like Rollout Routing Replay (R3) for efficient data handling.
Compare Miles
Compare with another toolMore Like This
