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Announcement

Miles v0.1.2 adds experimental Kubernetes support and a third training backend

The release announcement says models that learn from each other, such as a model and its grader, can train together.

Ying ShengYS
RadixArkRA
2 Sources, 1h ago, first seen 1h ago

TLDR

The Miles v0.1.2 announcement says its experimental Kubernetes backend lets reinforcement-learning jobs run as cluster workloads while orchestration restarts without stopping training. It says score centering keeps asynchronous training stable and GPUs busy, and PyTorch's torchtitan joins Megatron and FSDP as a third training backend. The announcement also lists DeepSeek-V4.1-Flash and MiMo-V2.6-Flash-RL as landing in main.

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1.5K

2 Sources, first seen 1h ago

35 likes2 comments10 saves11 reposts

Combined views

1.5K

2 Sources, first seen 1h ago

35 likes2 comments10 saves11 reposts

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2 Sources

RadixArk@radixarkMiles v0.1.2 is out. This release makes Miles steadier and easier to operate in production. Score centering keeps async RL stable and GPUs busy. Miles can now run natively on Kubernetes via an experimental backend. RL jobs become ordinary cluster workloads, and orchestration can restart while training continues. Training gets more flexible, too. Models that learn from each other, like a model and its grader, can train and improve together in one run. torchtitan from @PyTorch joins Megatron and FSDP as a third training backend, giving teams another PyTorch-native option for training at scale. DeepSeek-V4.1-Flash and MiMo-V2.6-Flash-RL land in main. Full release notes 👇1h
Ying Sheng@ying11231RT @radixark: Miles v0.1.2 is out. This release makes Miles steadier and easier to operate in production. Score centering keeps async RL…1h
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    2 Sources

    RadixArk@radixarkMiles v0.1.2 is out. This release makes Miles steadier and easier to operate in production. Score centering keeps async RL stable and GPUs busy. Miles can now run natively on Kubernetes via an experimental backend. RL jobs become ordinary cluster workloads, and orchestration can restart while training continues. Training gets more flexible, too. Models that learn from each other, like a model and its grader, can train and improve together in one run. torchtitan from @PyTorch joins Megatron and FSDP as a third training backend, giving teams another PyTorch-native option for training at scale. DeepSeek-V4.1-Flash and MiMo-V2.6-Flash-RL land in main. Full release notes 👇1h
    Ying Sheng@ying11231RT @radixark: Miles v0.1.2 is out. This release makes Miles steadier and easier to operate in production. Score centering keeps async RL…1h
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