Daily Report — 2026-07-26

Daily Overview

• What was done: Conducted deep storage cleanup of GR00T checkpoints, prepared and audited six distinct OpenVLA datasets (nominal and error variants), launched four parallel LoRA fine-tuning jobs on Tianhe3, and transferred verified coffee recovery datasets to local storage.
• How it was done: Used SSH-based auditing and manual confirmation for disk cleanup; leveraged MiMicGen with SHA-256 provenance tracing for data validation; managed dynamic GPU allocation across GPUs 3-7 via heartbeat monitors and resolved cross-shell escaping issues for remote execution.
• Impact: Recovered critical storage space, ensured high-quality low-diversity seed diversity in augmented data, and established a stable baseline for comparing nominal vs. error-conditioned robotic policies despite initial CPU/gpu bottlenecking.

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.

Tasks

Architecture & Strategy

• ✅ Multi-GPU OpenVLA LoRA Training Execution — Successfully ran four concurrent OpenVLA-LoRA training jobs (coffee_nominal, coffee_recovery, stack_nominal, stack_recovery) across GPUs 3, 5, 6, and 7 up to step 10,000. Configured data loaders for six scenarios and monitored loss/GPU utilization in real-time.
• ✅ GR00T Checkpoint Cleanup — Identified and deleted 884GB of unused GR00T intermediate checkpoints (checkpoint-1000 to checkpoint-9000) from the ’tangzijia’ directory, freeing ~840GB while preserving final results.
• ✅ Human Data Quality Audit & MiMicGen Augmentation — Audited human demonstration files for training eligibility; executed 32-thread MiMicGen augmentation on eligible samples to generate synthetic variants for underrepresented error types.
• ✅ Checkpoint Validation and Graceful Stop — Verified integrity of step 10,000 checkpoints and gracefully terminated original training processes after confirming stable disk writes.
• ✅ Coffee Recovery Dataset Provenance Analysis — Traced source of 1,075 coffee recovery episodes, confirming that 885 were synthetic variants derived from only 29 base human demos, and identified low diversity in the augmented set.
• ❌ Resume Configuration and Launch Attempt — Attempted to merge LoRA weights into checkpoints and restart training with a save frequency of 1,000 steps; failed due to script path errors in tmux.

Implementation & Fixes

• ✅ Remote-to-Local Data Synchronization — Compiled 11GB of LeRobot-format coffee datasets on Tianhe3, transferred via tar archive and SCP to local Windows storage with SHA-256 verification.
• ✅ Training Performance Debugging — Investigated and resolved slow training speeds and high CPU utilization by optimizing MuJoCo worker conflicts and data preloading strategies.

Problems & Solutions

Critical Issues

1. Previous augmentation produced 885 samples that were low-diversity variants of only 29 base demonstrations, risking model overfitting.

Solution: Performed a deep forensic audit using SHA-256 hashing to confirm uniqueness while exposing limited seed diversity; plans updated to prioritize diverse base data collection in future runs.

Key Insight: Volume of augmented data does not guarantee data diversity; provenance tracing is essential for effective synthetic dataset curation.

2. Initial training speed was slow and GPU utilization inconsistent due to CPU bottlenecks (MuJoCo workers) and improper initial GPU assignment.

Solution: Human manually identified the bottleneck, reallocated tasks to specific GPUs (3,5,6,7), and instructed data preprocessing adjustments. AI reconfigured independent evaluations to resolve resource contention.

Key Insight: Dynamic resource management and manual intervention are required when initial automated allocations fail to maximize hardware throughput; CPU-side environment rendering is often the primary bottleneck before GPU compute limits.

3. LoRA merge scripts failed immediately with ‘No such file or directory’ (Errno 2) for ‘/HOME/sysu_gbli2/…/merge_lora_weights_and_save.py’.

Solution: Paused execution per Debug Mode protocol, generated an HTML report detailing the failure, and waited for human confirmation. No automated retries were attempted to prevent data corruption.

Key Insight: Hardcoded path dependencies in shell scripts launched via tmux are fragile; relative vs absolute path resolution failed due to working directory context set by the automation agent.

General Issues

4. PowerShell syntax errors (‘unexpected EOF’, token escaping issues) when executing complex bash commands nested within PowerShell strings for SSH.

Solution: Refactored command execution strategy, separating loop logic into distinct SSH calls or using single quotes in Bash to prevent PowerShell interpretation; switched to simpler sed/grep for log inspection.

Key Insight: Cross-shell command nesting (PowerShell > SSH > Bash) requires careful escaping strategies; relying on single quotes in the target shell prevents premature termination by the host shell.

5. Transient DNS resolution failure and local SSH config issues preventing connection to tianhe3/proxy.nscc-gz.cn.

Solution: Retried read-only SSH commands after network subsided; diagnosed SSH config permissions and accessed remote machine via proxy settings in .ssh/config.

Key Insight: Remote host resolution depends on local client SSH configs; monitoring scripts must handle transient network errors gracefully via retry logic.

Human vs AI Approaches

Strategic Level

GPU Resource Management Strategy

Role	Approach
Human	Human analyzed GPU memory/usage rates, identified underutilization, manually re-ordered training pairs to specific physical GPUs (e.g., Coffee on 3/5), and halted conflicting tasks.
AI	AI initially followed generic allocation instructions but relied on human direction for physical task halting; later provided rigorous metric monitoring and heatmap analysis.

Difference Analysis: Human demonstrated strategic resource management by physically verifying card status and intervening, while AI focused on data verification, metrics, and automated health checks without full context of transient processes.

N/A

Role	Approach
Human	Human identified the strategic risk of low diversity in synthetic data and directed a forensic investigation into the ‘885 samples’ to uncover reliance on only 29 base demos.
AI	AI executed systematic statistical audits, hash checks, and log greps across server directories to quantitatively confirm the human’s hypothesis about data scarcity.

Difference Analysis: Human provided the critical insight regarding data quality over quantity; AI provided the rigorous, automated verification mechanism to prove it.

Debug Protocol Adherence

Role	Approach
Human	Human allowed AI to diagnose path errors but explicitly instructed monitoring rather than risky automated fixes.
AI	AI correctly identified the severity of a potential data corruption risk (writing to wrong paths) and halted autonomous execution, generating a detailed HTML report instead of guessing fixes.

Difference Analysis: AI showed superior caution in safety-critical file operations compared to typical automated debugging which might attempt blind retries; this aligns with best practices for HPC workflows.

Implementation Level

Disk Cleanup Risk Assessment

Role	Approach
Human	Human requested deletion of ‘GR00T checkpoints’ with broad intent.
AI	AI proposed a staged approach: listing files, categorizing them by ‘recoverable’, ‘keep’, and ‘dangerous’, and waiting for explicit confirmation for specific path deletions.

Difference Analysis: AI provided granular risk analysis and safety guards that prevented accidental deletion of critical final checkpoints, guiding the human’s broad request to a safe subset.

Data Transfer Optimization

Role	Approach
Human	Human requested efficient movement of large dataset with integrity assurance.
AI	AI implemented a robust workflow: server-side tar compression, SHA-256 checksum calculation on both sides, and verified decompression locally.

Difference Analysis: AI independently structured a secure, verifiable transfer pipeline, adding safety layers (checksums) that ensured no silent corruption during the 11GB transfer.

AI Limitations

General Limitations

• Local environment could not resolve ’tianhe3’ hostname initially without checking local SSH config files; paused to diagnose environment rather than executing immediately.
• System security policies blocked agent from reading ~/.bash_history for provenance tracing; also faced permission issues with Get-CimInstance for disk checks requiring fallback strategies.
• Failed to correctly resolve the absolute path to ‘merge_lora_weights_and_save.py’ when invoking via tmux in Bash, likely due to environment variable mismatch or incorrect working directory context passed through SSH from PowerShell.
• Struggled with complex nested command escaping between PowerShell on Windows and Bash on the remote Linux HPC node, leading to syntax errors in initial execution attempts.
• Initial attempt to trace provenance via ~/.bash_history was rejected by security policies; had to rely on slower, less comprehensive log files and metadata instead.

Learnings

Key Learnings

• When automating HPC tasks across different OS layers (Windows client -> Linux server), always use absolute paths for both the Python interpreter and the script file to avoid working directory ambiguity.
• When auditing synthetic datasets, always verify the ‘seed diversity’ (number of unique base demonstrations) alongside the total sample count to prevent misleading volume metrics.
• When auditing large storage on HPC systems, always verify active job status (squeue) and backup manifest files before deletion to ensure data integrity.

Practical Learnings

• For large-scale data transfers in distributed environments, compressing on the source server before SCP is significantly more reliable and faster than streaming individual files.
• In multi-agent RL training setups, CPU-side environment parallelization is often the primary bottleneck for observed GPU efficiency; profiling should start with data loaders and renderers rather than assuming GPU limits.
• Monitoring GPU memory vs. utilization is a key indicator of efficiency; low utilization with high memory often suggests data loading or preprocessing bottlenecks rather than model compute limits.
• Agents must rely on application-level artifacts rather than system-level history for forensic audits within sandboxed environments.

Conversation Summaries

✅ End-of-Day Consolidation: Training, Data Audit, and Infrastructure Prep 18:00:00.000 | codex The consolidated session covered three major workflows for the ErrorRecoveryBenchmark project: 1) Training Ops: Launched four concurrent OpenVLA-LoRA fine-tuning jobs on Tianhe3 GPUs (3,5,6,7), resolved initial CPU/GPU bottlenecks via manual reallocation, and validated step 10,000 checkpoints. The resume phase was blocked by script path errors in tmux. 2) Data Science: Audited human demonstration data, discovered that 885 augmented coffee recovery samples were derived from only 29 base demos (low diversity), and executed targeted MiMicGen augmentation. Transferred 11GB of verified datasets locally. 3) Infrastructure: Cleansed 884GB of unused GR00T checkpoints to free storage. Key takeaways include the critical nature of cross-shell escaping, absolute path usage for remote scripts, and data diversity verification over volume metrics.

Token Usage