Weekly Report — 2026-W30 (2026-07-20 ~ 2026-07-26)

This week focused on stabilizing and validating the ErrorRecoveryBenchmark dataset, executing NIPS rebuttal experiments (OpenVLA-OFT and GR00T), preparing quantized model benchmarks for Qualcomm hardware, and resolving complex infrastructure bottlenecks. Key achievements include rectifying critical data leakage issues in training/evaluation splits, successfully launching multi-GPU LoRA training for OpenVLA with validated data provenance, and establishing robust evaluation pipelines for pi0.5 and GR00T on both local clusters and HPC infrastructure.

Weekly Overview

Metric	Value
Date Range	2026-07-20 ~ 2026-07-26
Active Days	6 / 7
Total Conversations	14
Projects	9
Tasks Completed	30
Tasks In Progress	6
Total Tokens	701,366,311
Total Cost	$526.67
Claude Code Token	978,767
Claude Code Cost	$3.05
Codex Token	700,387,544
Codex Cost	$523.62
Daily Average Cost	$131.67

Project Progress

ErrorRecoveryBenchmark & NIPS Rebuttal (4 days active) — 🔄 active

Accomplishments:

Audited and fixed false-negative logic in recovery demos, recovering 555 valid trajectories.
Identified and disclosed data leakage between training/evaluation sets due to shared clean trajectories.
Prepared response strategy for NIPS reviewers regarding dataset splits and policy coverage.

Blockers:

⚠️ AI security policies blocked bulk upload of 8GB dataset archives to HuggingFace, requiring manual handling.

OpenVLA-OFT Training (3 days active) — 🔄 active

Accomplishments:

Audited six distinct datasets (nominal/error variants) and verified source diversity via SHA-256 hashing.
Executed four concurrent LoRA training jobs on Tianhe-3 GPUs, reaching step 10,000.
Optimized data pipelines to resolve initial low GPU utilization caused by CPU/MuJoCo bottlenecks.

Blockers:

⚠️ LoRA merge scripts failed due to path resolution errors in tmux environments.
⚠️ Low seed diversity identified in 885 augmented samples (derived from only 29 base demos).

pi0.5 & GR00T Quantization & Evaluation (3 days active) — 🔄 active

Accomplishments:

Fixed critical silent bypass bug in GR00T’s Qwen3VL integration.
Defined validation frameworks (monotonic error/FP64 equivalence) for quantization accuracy.
Identified FP16 backbone incompatibility with Qualcomm IQ-9075 NPU due to binary size limits (~3.78GB > 3.67GB limit).

Blockers:

⚠️ Silent bypass bug in GR00T evaluation pipeline.
⚠️ SpinQuant latency overhead analysis required deep profiling.

Infrastructure & Tooling (2 days active) — ✅ completed

Accomplishments:

Cleared 840GB of stale GR00T checkpoints on HPC.
Restored SSH connectivity and resolved stale port bindings on Tianhe-3.
Completed Video OCR pipeline with fuzzy deduplication.

Blockers:

⚠️ Complex cross-shell command escaping (PowerShell/SSH/Bash).
⚠️ AI agent limitations in handling large binary files and nested shell syntax.

Key Tasks

✅ Audit Train/Eval Data Leakage (2026-07-22) — Traced code to confirm shared clean trajectory lineage between training/evaluation sets, necessitating disclosure for NIPS rebuttal.
✅ ErrorRecoveryBenchmark Data Audit & Reconciliation (2026-07-23) — Fixed audit script logic to recover 555 high-confidence trajectories and aligned local/remote dataset states.
✅ quantize-MVP Repository Curation & Migration Prep (2026-07-21) — Refactored codebase into minimal reproducible MVP, removed cluster dependencies, and prepared for A100 migration.
✅ Multi-GPU OpenVLA LoRA Training Execution (2026-07-26) — Ran four concurrent LoRA jobs up to step 10,000 with verified checkpoints and optimized data loaders.
✅ GR00T Evaluation Pipeline Fix (2026-07-20) — Corrected PyTorch attribute shadowing that caused silent model bypass bugs in Qwen3VL integration.
✅ pi0.5 FP16 Quantization & Profiling Analysis (2026-07-21) — Confirmed structural incompatibility of FP16 backbone with IQ-9075 NPU; established need for A100 for full-fidelity quantization.
🚫 Resolve pi0.5 GPU Matrix Task Failure (2026-07-22) — Background task for pi0.5 matrix operation failed with exit code 1; requires investigation into GPU resource allocation.
✅ Human Data Quality Audit & MiMicGen Augmentation (2026-07-26) — Audited human demos for eligibility and executed augmentation; identified low seed diversity in synthetic variants.

Problems & Solutions

1. Audit Script False Negatives: Verification logic incorrectly reset simulation to error state instead of start state, rejecting valid demos. [ErrorRecoveryBenchmark] (2026-07-23)

Solution: Modified audit script to replay from $states[0] of collected NPZ files, recovering 555 valid trajectories.

2. Data Leakage via Shared Clean Trajectories: Training and evaluation sets shared source clean trajectories, violating split integrity. [ErrorRecoveryBenchmark] (2026-07-24)

Solution: Validated lineage via JSONL manifests; confirmed 100% overlap in source data, prompting disclosure in rebuttal and future trajectory-level splitting.

3. pi0.5 FP16 Backbone Incompatibility: Context binary size (3.78GB) exceeded IQ-9075 NPU limit (3.67GB), preventing execution. [pi0.5 Quantization] (2026-07-21)

Solution: Confirmed incompatibility via QNN logs; determined quantization or splitting is mandatory for this hardware.

4. OpenVLA Low GPU Utilization: Slow training speeds and high CPU load indicated data pipeline bottlenecks, not GPU limits. [OpenVLA Training] (2026-07-25)

Solution: Optimized MuJoCo workers and data preprocessing; reallocated GPUs to resolve resource contention.

5. GR00T Silent Bypass Bug: Quantized models reported 100% success due to PyTorch attribute shadowing in Qwen3VL. [GR00T Evaluation] (2026-07-20)

Solution: Identified nn.Module.__setattr__ intercepting assignments; fixed by verifying submodule identity at assignment time.

6. SSH Reverse Proxy Failures: Stale zombie processes held default ports on Tianhe-3, blocking connectivity. [Infrastructure] (2026-07-23)

Solution: Updated SSH config to use ephemeral ports (10090+) with ServerAliveInterval and ExitOnForwardFailure.

7. Low Diversity in Augmented Data: 885 synthetic samples derived from only 29 base human demos. [OpenVLA Data] (2026-07-26)

Solution: Used SHA-256 hashing to verify provenance; concluded need for more diverse base data collection in future runs.

Learnings

Debugging (debugging)

In simulation-based robotics, audit logic must align with the controller’s initial reset state; mismatches cause catastrophic data loss. Identity assertions (assert x is y) are robust defenses against property getter bypasses.
Auditing large-scale scientific datasets requires verifying raw file system state, audit JSONs, and release manifests to catch metric definition mismatches. Shared source clean trajectories constitute data leakage even if injected errors are unique.

Architecture (architecture)

FP16 models requiring >3.67GB context binary cannot run on Qualcomm IQ-9075 NPU. Low GPU utilization in VLA training often indicates CPU-side data loading/rendering bottlenecks rather than compute limits.
For robust synthetic data generation, audit ‘seed diversity’ (unique base samples) alongside total sample count. Volume alone does not guarantee model generalization.

Tools (tools)

Multi-layered shell executions (PowerShell -> SSH -> Bash) require careful escaping; using absolute paths and native CLI tools like jq is more reliable than complex inline Python/PowerShell commands.
AI safety filters may block legitimate bulk data uploads (e.g., to HF) if they perceive external destinations as untrusted; manual handling or checkpoint-resuming strategies are often required.

AI Usage Notes

Effective Patterns:

✓ Using multi-agent orchestration (Grok/Fable/Cursor) for scientific protocol auditing improved rigor and prevented label leakage.
✓ SHA-256 provenance tracing was highly effective for verifying data integrity and uniqueness in large-scale augmentation pipelines.
✓ Cross-referencing local/remote states via iterative SSH commands resolved significant dataset discrepancy issues.

Limitations:

✗ AI agents struggled with complex nested shell escaping (PowerShell/SSH/Bash) and frequently produced syntax errors.
✗ Agents lacked ability to automatically resolve stale HPC port bindings or infer hostname resolution for internal clusters without explicit config.
✗ AI safety filters blocked legitimate bulk data uploads, requiring manual intervention.

Next Week Outlook

Focus on resuming and optimizing OpenVLA-OFT training after the LoRA merge/step-10,000 checkpoint interval. Continue investigating the pi0.5 GPU matrix task failure (exit code 1) by validating GPU resource states and kernel execution environments. Conduct preliminary environment validation for new OpenVLA-OFT experiments on Tianhe-3 following the data provenance audit. Address remaining GR00T latency profiling results and finalize the MVP repository structure for public sharing.

Token Usage Statistics

Peak Day: unknown — $288.00 / 396.6M tokens

Daily Average: $131.67

🤖 This entry was generated by AI from conversation logs and may contain errors.

Weekly Report — 2026-W30 (2026-07-20 ~ 2026-07-26)#

Weekly Overview#

Project Progress#

ErrorRecoveryBenchmark & NIPS Rebuttal (4 days active) — 🔄 active#

OpenVLA-OFT Training (3 days active) — 🔄 active#

pi0.5 & GR00T Quantization & Evaluation (3 days active) — 🔄 active#

Infrastructure & Tooling (2 days active) — ✅ completed#

Key Tasks#

Problems & Solutions#

1. Audit Script False Negatives: Verification logic incorrectly reset simulation to error state instead of start state, rejecting valid demos. [ErrorRecoveryBenchmark] (2026-07-23)#

2. Data Leakage via Shared Clean Trajectories: Training and evaluation sets shared source clean trajectories, violating split integrity. [ErrorRecoveryBenchmark] (2026-07-24)#

3. pi0.5 FP16 Backbone Incompatibility: Context binary size (3.78GB) exceeded IQ-9075 NPU limit (3.67GB), preventing execution. [pi0.5 Quantization] (2026-07-21)#

4. OpenVLA Low GPU Utilization: Slow training speeds and high CPU load indicated data pipeline bottlenecks, not GPU limits. [OpenVLA Training] (2026-07-25)#

5. GR00T Silent Bypass Bug: Quantized models reported 100% success due to PyTorch attribute shadowing in Qwen3VL. [GR00T Evaluation] (2026-07-20)#

6. SSH Reverse Proxy Failures: Stale zombie processes held default ports on Tianhe-3, blocking connectivity. [Infrastructure] (2026-07-23)#

7. Low Diversity in Augmented Data: 885 synthetic samples derived from only 29 base human demos. [OpenVLA Data] (2026-07-26)#

Learnings#

Debugging (debugging)#

Architecture (architecture)#

Tools (tools)#

AI Usage Notes#

Next Week Outlook#

Token Usage Statistics#

Weekly Report — 2026-W30 (2026-07-20 ~ 2026-07-26)

Weekly Overview

Project Progress

ErrorRecoveryBenchmark & NIPS Rebuttal (4 days active) — 🔄 active

OpenVLA-OFT Training (3 days active) — 🔄 active

pi0.5 & GR00T Quantization & Evaluation (3 days active) — 🔄 active

Infrastructure & Tooling (2 days active) — ✅ completed

Key Tasks

Problems & Solutions

1. Audit Script False Negatives: Verification logic incorrectly reset simulation to error state instead of start state, rejecting valid demos. [ErrorRecoveryBenchmark] (2026-07-23)

2. Data Leakage via Shared Clean Trajectories: Training and evaluation sets shared source clean trajectories, violating split integrity. [ErrorRecoveryBenchmark] (2026-07-24)

3. pi0.5 FP16 Backbone Incompatibility: Context binary size (3.78GB) exceeded IQ-9075 NPU limit (3.67GB), preventing execution. [pi0.5 Quantization] (2026-07-21)

4. OpenVLA Low GPU Utilization: Slow training speeds and high CPU load indicated data pipeline bottlenecks, not GPU limits. [OpenVLA Training] (2026-07-25)

5. GR00T Silent Bypass Bug: Quantized models reported 100% success due to PyTorch attribute shadowing in Qwen3VL. [GR00T Evaluation] (2026-07-20)

6. SSH Reverse Proxy Failures: Stale zombie processes held default ports on Tianhe-3, blocking connectivity. [Infrastructure] (2026-07-23)

7. Low Diversity in Augmented Data: 885 synthetic samples derived from only 29 base human demos. [OpenVLA Data] (2026-07-26)

Learnings

Debugging (debugging)

Architecture (architecture)

Tools (tools)

AI Usage Notes

Next Week Outlook

Token Usage Statistics