feat: add MLX backend for local Apple Silicon RL training#15
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alvin-chang wants to merge 3 commits intoaiming-lab:mainfrom
Open
feat: add MLX backend for local Apple Silicon RL training#15alvin-chang wants to merge 3 commits intoaiming-lab:mainfrom
alvin-chang wants to merge 3 commits intoaiming-lab:mainfrom
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- metaclaw/mlx_backend/: Tinker-compatible adapter package (ServiceClient, SamplingClient, LoraTrainingClient) backed by mlx and mlx-lm - metaclaw/sdk_backend.py: auto-detection falls through to MLX when no cloud credentials are set - metaclaw/api_server.py: guard run_llm when no llm_api_key configured; skip Tinker-specific sample_async kwargs for MLX - metaclaw/setup_wizard.py: add 'mlx' backend choice, skip API key prompts, validate mlx/mlx-lm install, default to HF MLX model ID - tests/: end-to-end smoke test + unit tests (16/16 passing)
ImKeTT
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Mar 16, 2026
ImKeTT
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Mar 16, 2026
ImKeTT
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Mar 16, 2026
ImKeTT
reviewed
Mar 16, 2026
ImKeTT
reviewed
Mar 16, 2026
ImKeTT
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Mar 16, 2026
ImKeTT
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Mar 16, 2026
Collaborator
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Hi, @alvin-chang, thank you for contributing, the code looks generally good. Only minor changes are needed to be compatible with the style of the codebase :) |
Collaborator
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Hi @alvin-chang, friendly ping here to make sure that you are still working on this PR |
Author
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Let me remove those and resubmit the PR. |
Collaborator
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Hi @alvin-chang are you still working on this PR? I saw there's one last comment remaining unresolved. Feel free to ping me once you finished all the changes |
Author
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I thought I resolved all of them. Is it the METACLAW_RL_BACKEND? |
Collaborator
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ah it's the INTEGRATION_NOTES.md. could you integrate it into a small section in readme? |
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"Adds a local RL training backend using Apple MLX, so MetaClaw can run
the full train loop (inference → GRPO → weight update → hot-swap) on
Apple Silicon without any cloud API.
Changes (13 files, +1805 -39)
Testing
```bash
MLX_TEST_MODEL=mlx-community/Qwen2.5-1.5B-Instruct-4bit python3 tests/smoke_mlx_proxy.py
```
16/16 passing: auto-detection → model load → proxy startup → chat completions
with logprobs → sample collection → forward/backward + optim → hot-swap → post-swap inference."