Daily Summaries

Resolved critical silent quantization failures in the pi0.5 pipeline and finalized progress reports for MIHD research while establishing the foundational infrastructure and MVP design for RoboMemory’s visual memory experiments.

Resolved critical GR00T W4 quantization accuracy collapse by fixing RMSNorm configuration and validated deployability; completed Phd application strategy audit and MIHD progress report overhaul with full-slide clustering; optimized TokenMonitor rate-limiting and ErrorRecoveryBenchmark repository state with significant remote disk cleanup.

Resolved critical OS-level instabilities (Windows crashes, macOS rate-limiting) while delivering significant codebase improvements including bilingual content leakage fixes for the Hugo blog, Windows UI parity with high-DPI support for LiveCaption, and architectural documentation for VLA quantization projects.

Switched Claude Code authentication from AWS Bedrock to direct Claude subscription across all devices (MacOS, Lighthouse, Athena, TzJsDesktop).

Completed repository maintenance and configuration restoration across multiple GitHub projects, while orchestrating and debugging distributed OpenVLA multi-GPU training experiments on the Tianhe3 cluster.

Standardized and rebuilt AI configuration across 15 GitHub repositories via automated workflows, while conducting critical error recovery analysis for the ErrorRecoveryBenchmark project.

每日 AI 对话总结

Identified closed-loop covariate shift as the root cause of zero success in nominal-only policies via rigorous paired evaluation and weight verification, while simultaneously establishing robust Stack Three infrastructure and parallel training pipelines for six Robosuite tasks.

Executed and monitored four parallel OpenVLA-LoRA training runs on Tianhe3 up to 20k steps, implementing disk-fault-tolerant checkpointing, while conducting forensic diagnostics that identified covariate shift as the primary cause of Coffee Nominal evaluation failure.

Executed and optimized multi-GPU OpenVLA LoRA training, audited data provenance with MiMicGen augmentation, and cleared ~840GB of legacy checkpoints to establish a robust foundation for error recovery policy learning.