Worked on the volcengine/verl repository to address stability issues in VLM SFT training under FSDP, specifically when using DatasetPadMode.NO_PADDING. Delivered a targeted fix for a PyTorch NestedTensor jagged dimension ambiguity that caused intermittent shape mismatches during training. The solution involved replacing the use of .values() with a combination of unbind() and cat() operations, ensuring correct tensor shapes across varying micro-batch scenarios. This internal change improved training reliability without altering the public API. The work demonstrated strong skills in Python, data processing, and advanced PyTorch tensor manipulation, with disciplined code review and comprehensive validation across diverse training conditions.