Add checkpointing support for Tinker SkyRL backend#992
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tyler-griggs merged 4 commits intomainfrom Jan 30, 2026
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Add full checkpoint save/load functionality to SkyRLTrainBackend: - save_checkpoint(): Saves model + optimizer + scheduler state as uncompressed tar - load_checkpoint(): Restores full training state from tar checkpoint - save_sampler_checkpoint(): Exports model weights in HuggingFace format for inference Implementation wraps WorkerDispatch checkpoint methods and handles tar packaging. Uses uncompressed tar to avoid 5-10 minute gzip bottleneck on 6-7GB FSDP checkpoints. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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This pull request introduces checkpointing capabilities to the SkyRLTrainBackend, enabling training state to be saved and restored via tar archives. However, a high-severity path traversal vulnerability was identified in the checkpoint loading logic due to the unsafe use of tarfile.extractall(). Additionally, the worker initialization logic uses trust_remote_code=True when loading model configurations, which poses a significant security risk if untrusted model identifiers are processed. Beyond security, there's also a minor race condition and some code duplication that could benefit from refactoring for improved maintainability.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Address PR review feedback: 1. Security: Add filter='data' to tarfile.extractall() to prevent path traversal (TarSlip) attacks where malicious archives could write outside the temp directory 2. Refactor: Extract duplicate validation logic into _validate_model_state() helper method (used by all 3 checkpoint methods) 3. Remove redundant os.path.exists() check that creates TOCTOU race condition - tarfile.open() already raises FileNotFoundError 4. Refactor: Extract common tar creation logic into _create_tar_from_directory() helper method to reduce duplication Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Summary
Implements checkpointing for the SkyRL-Train backend in Tinker, enabling periodic checkpoint saves and training resumption.
Changes
Adds three checkpoint methods to
SkyRLTrainBackend:save_checkpoint()- Saves full training state (model + optimizer + scheduler) as tar archiveload_checkpoint()- Restores full training state from checkpointsave_sampler_checkpoint()- Exports model-only checkpoint in HuggingFace formatImplementation
WorkerDispatch.save_checkpoint()andload_checkpoint()methods