Daily Report — 2026-07-24

Daily Overview

• What was done: Performed forensic data auditing for a NIPS rebuttal, monitored four concurrent OpenVLA training campaigns, and validated the Tianhe-3 HPC environment for new OpenVLA-OFT experiments.
• How it was done: Executed read-only SSH commands to inspect JSONL metadata and lineage manifests on Tianhe-3; utilized Codex agents to check GPU states, logs, and Python package dependencies in the conda environment.
• Impact: Identified 100% clean trajectory overlap between train/eval sets for honest rebuttal response, confirmed healthy status of four-GPU training jobs, and established dependency baselines for OpenVLA-OFT while noting path sync issues.

Completed a critical data leakage audit for the ErrorRecoveryBenchmark rebuttal, verified the health and utilization of four active OpenVLA training jobs, and conducted environment validation for OpenVLA-OFT LoRA setup on Tianhe-3.

Tasks

Architecture & Strategy

• ✅ Audit Train/Eval Data Leakage — Analyzed training and evaluation datasets on Tianhe-3 to determine if clean trajectories, object configurations, or post-error states were leaked between splits; confirmed 100% overlap in source clean trajectories.
• ✅ Rebuttal Response Strategy — Formulated responses for NIPS reviewers regarding missing supplements, limited policy coverage, and data split integrity based on audit findings.
• ✅ Monitor OpenVLA Training Jobs — Performed final status checks on four active OpenVLA training jobs, verifying scheduler states, GPU utilization, and log absence of failure markers across multiple checkpoints.
• 🔄 OpenVLA-OFT Training Setup Investigation — Verified data availability and environment compatibility on Tianhe-3 for OpenVLA-OFT LoRA training across six mimicgen domains; identified stale editable install paths.

Implementation & Fixes

• ✅ Dataset Structure Validation — Checked directory structures and metadata files for six nominal and six error-included datasets to ensure compatibility with LeRobot/OpenVLA loaders.
• ✅ Dependency Inventory — Confirmed installed versions of PyTorch, Transformers, PEFT, and OpenVLA-OFT in the conda environment on Tianhe-3.
• ✅ Fix DOCX Rendering Script — Diagnosed and patched render_docx_with_word.ps1 to handle absolute vs. relative output paths correctly during Word automation for NIPS rebuttal processing.

Problems & Solutions

Critical Issues

1. Reviewer concern regarding data leakage between training and evaluation sets for ErrorRecoveryBenchmark; potential overlap in physical scenes or trajectories.

Solution: Traced code to confirm both generators read from shared clean trajectory folders. Queried multiple JSONL manifests on Tianhe-3 to discover 100% overlap in source clean trajectories. Verified that while exact post-error fingerprints showed some overlap, the critical issue was the shared lineage, necessitating disclosure.

Key Insight: Sharing source clean trajectories constitutes data leakage even if injected error states are unique; future splits must occur at the trajectory level before generation.

2. Command execution errors in remote shell due to syntax mismatch: ‘unexpected EOF while looking matching "' when embedding bash commands in Python tool calls.

Solution: Corrected JSON escaping in arguments, ensuring quotes were properly escaped for bash command injection on the remote host.

Key Insight: Explicit quote escaping is critical when embedding shell scripts within Python JSON structures; single-pass interpretation often fails if local/remote parsing rules differ.

General Issues

3. Target directory and editable installation path for OpenVLA-OFT returned ‘No such file or directory’ during GIT/Pip status checks on Tianhe-3.

Solution: Identified that the local editable installation path was stale or unsynced. Pivoted to inspecting site-packages and using pip show -f to locate actual executable scripts, as hard-coded paths from other setups are unreliable.

Key Insight: Editable installs often drift from source repos; always verify pip output against actual file system existence when debugging missing dependencies.

4. PowerShell script failed with ‘path format not supported’ and path handle bugs in automation tools due to incorrect JoinPath logic for absolute directories.

Solution: Patched render_docx_with_word.ps1 to check if output directory is rooted before joining; used GetFullPath directly for absolute paths. Resolved permission denied exceptions on CIM instances during PDF processing.

Key Insight: Absolute paths passed to path-utility functions require conditional routing for rooted vs. relative paths to avoid double-resolution errors.

5. AI initial SSH failure to resolve ’tianhe3’ hostname automatically.

Solution: Resolved by providing explicit host keys or aliases, as the agent lacked automatic DNS resolution for internal cluster names.

Key Insight: Autonomous agents may lack pre-configured DNS or SSH aliases for private HPC clusters.

Human vs AI Approaches

Strategic Level

Data Leakage Investigation Methodology

Role	Approach
Human	The human requested a specific comparison of clean trajectories and post-error states to address reviewer concerns, driving the investigation towards lineage verification.
AI	The AI implemented a rigorous read-only SSH strategy, querying existing JSONL manifests without modifying the dataset, focusing on evidence gathering.

Difference Analysis: Human defined the strategic goal (answering the reviewer), while AI adopted an operational constraint (Read-Only) that prevented accidental data modification during the sensitive audit.

Training Strategy Formulation vs. Infrastructure Check

Role	Approach
Human	User explicitly requested finding pre-existing Robosuite LoRA configurations and copying parameters, indicating a strategy of leveraging established benchmarks.
AI	AI focused on technical feasibility checks (SSH, env versions) and broad literature search but did not proactively locate specific existing config files as requested.

Difference Analysis: Human intent was strategic replication; AI interpreted the request primarily as an infrastructure readiness check. Human domain knowledge guided the scope more than generic AI advice.

Implementation Level

Path Handling and Automation Logic

Role	Approach
Human	Human treated file paths as generic strings across different OS environments (Windows PowerShell vs. Linux Bash).
AI	AI identified type-specific API constraints, writing conditional logic for absolute/relative paths in PowerShell and handling quote escaping for nested bash calls.

Difference Analysis: AI recognized OS-specific parsing rules and exception handling requirements that human implicit assumptions overlooked.

AI Limitations

Critical Limitations

• AI initially failed to locate exact ‘OpenVLA-OFT Robosuite LoRA’ pre-trained config or code snippets via web search, indicating difficulty accessing niche, recent, or unindexed repository details without direct codebase access.

General Limitations

• AI struggled with complex nested shell quoting in automated tool calls, leading to syntax errors that required iterative correction.
• AI failed to resolve ’tianhe3’ hostname via SSH automatically, suggesting it lacks configuration for internal cluster DNS/SSH aliases.

Learnings

Key Learnings

• Sharing source clean trajectories between training and evaluation datasets effectively constitutes data leakage, requiring trajectory-level splitting before any error injection or scene generation.
• In remote HPC debugging via AI agents, always explicitly verify synchronization between pip editable installs and actual file system paths, as they frequently drift.

Practical Learnings

• For niche models like OpenVLA-OFT, relying on ‘what others have done’ requires direct codebase inspection rather than general web searches, which may return tangentially related works.
• When auditing large-scale scientific datasets, relying on auxiliary qualification manifests (e.g., training_lineage.jsonl) is more reliable and faster than re-running generation pipelines or scanning raw scene files.

Conversation Summaries

ErrorRecoveryBenchmark

✅ NIPS Rebuttal Admin, DOCX Fix & Forensic Data Leakage Audit 06:19:38.474 | codex The session combined administrative tasks for NIPS rebuttals with critical forensic auditing. Initial efforts fixed render_docx_with_word.ps1 path handling bugs and permission issues for document processing. Subsequently, the team conducted a deep audit of the ErrorRecoveryBenchmark to address reviewer concerns about data leakage. By tracing lineage manifests on Tianhe-3, it was discovered that 100% of source clean trajectories for the training set overlapped with the evaluation set. This finding invalidates claims of independent splits and necessitates a transparent rebuttal strategy regarding shared trajectory lineages.

OpenVLA-Training

🔄 OpenVLA Training Monitor & OpenVLA-OFT Environment Validation 15:50:24.295 | codex Activities included monitoring four active four-GPU OpenVLA training jobs, confirming healthy scheduler states and GPU utilization with no failure markers. Concurrently, preliminary investigation for OpenVLA-OFT LoRA training on Tianhe-3 was conducted. This involved validating dataset structures (LeRobot format) and inventorying Python dependencies (PyTorch, PEFT). A key finding was the discrepancy between the expected editable installation path and the actual file system, requiring a shift to package-based introspection for locating scripts.

Token Usage