Weekly Report — 2026-W31 (2026-07-27 ~ 2026-08-02)

This week focused on diagnosing fundamental failure modes in OpenVLA-LoRA training and establishing robust evaluation infrastructure. The primary achievement was identifying ‘covariate shift’ as the root cause of zero-success rates in nominal-only policies, confirming that recovery-augmented data is critical for closed-loop stability. Simultaneously, maintenance tasks included rebuilding global AI configuration standards across 15 repositories and restoring the BetterSSH workspace, while continuing intensive training experiments on the Tianhe3 cluster.

Weekly Overview

Metric	Value
Date Range	2026-07-27 ~ 2026-08-02
Active Days	4 / 7
Total Conversations	9
Projects	5
Tasks Completed	15
Tasks In Progress	2
Total Tokens	333,545,535
Total Cost	$283.74
Claude Code Token	32,278,096
Claude Code Cost	$77.93
Codex Token	301,267,439
Codex Cost	$205.81
Daily Average Cost	$56.75

Project Progress

OpenVLA-LoRA Training & Evaluation (4 days active) — 🔄 active

Accomplishments:

• Identified covariate shift as the cause of Coffee Nominal failure (zero success).
• Established robust disk-fault-tolerant checkpointing fallback mechanisms.
• Launched parallel evaluations for six Robosuite tasks on Tianhe3.
• Validated evaluation infrastructure via automated JSON integrity checks.

Blockers:

• ⚠️ Missing OPENVLA_OFT_ROOT environment variable causing initial crash loops.
• ⚠️ Disk quota exhaustion requiring manual cleanup and /tmp fallback logic.
• ⚠️ Complex PowerShell-to-Bash SSH escaping issues for remote execution.

Repository Maintenance & Configuration (2 days active) — ✅ completed

Accomplishments:

• Standardized CLAUDE.md and AGENTS.md across 15 GitHub repositories.
• Rebuilt BetterSSH pnpm workspace configuration from individual package contexts.
• Synchronized and merged PRs for 7 repositories to unify main branches.

Blockers:

• ⚠️ Security classifiers blocking auto-merge of high-risk git workflows.
• ⚠️ Stale documentation in legacy config files requiring complete replacement.

Key Tasks

• ✅ Coffee Nominal Zero Success Root Cause Analysis (2026-07-28) — Investigated 0% success rate by ruling out pipeline bugs and identifying closed-loop covariate shift. Proved that tiny initial biases compound in off-manifold states, requiring recovery data.
• 🔄 OpenVLA-LoRA Training Management (2026-07-31) — Orchestrated six concurrent training jobs on Tianhe3. Resolved missing environment variables via patching launch scripts and monitored progress via remote SSH.
• ✅ Global Repository Configuration Rebuild (2026-07-30) — Deleted obsolete configs across 15 repos and rebuilt canonical CLAUDE.md/AGENTS.md files using parallel AI agents to ensure up-to-date guidance.
• ✅ BetterSSH Workspace Restoration (2026-07-31) — Reconstructed missing root config files (package.json, pnpm-workspace.yaml) by analyzing individual package dependencies and fixed ESM/CJS interop issues.
• ✅ LoRA Checkpoint Merging and Evaluation Deployment (2026-07-27) — Merged LoRA weights for four task checkpoints, resolved Python path dependencies, and launched parallel VLA evaluation servers.

Problems & Solutions

1. Coffee Nominal policy exhibited 0% success rate despite low training loss, initially suspected as pipeline bugs or checkpoint corruption. [OpenVLA-LoRA Training & Evaluation] (2026-07-28)

Solution: Performed paired evaluation against Recovery data and verified weight deltas. Identified ‘closed-loop covariate shift’ where deviation from nominal trajectories leads to unrecoverable states due to lack of recovery examples in training data.

2. Primary disk space quota exceeded during checkpoint saving, risking training interruption on Tianhe3. [OpenVLA-LoRA Training & Evaluation] (2026-07-27)

Solution: Implemented try-except logic to fallback to /tmp if primary storage fails. User later cleaned 700GB of disk space to restore default behavior and ensure long-term stability.

3. OpenVLA training jobs crashed due to missing ‘OPENVLA_OFT_ROOT’ environment variable, appearing after import success. [OpenVLA-LoRA Training & Evaluation] (2026-07-31)

Solution: Identified the missing variable via traceback logs and applied a patch to export the correct path in the launch script, allowing successful job resubmission.

4. betterSSH root workspace files were lost during repo move, breaking pnpm builds and typechecks. [Repository Maintenance & Configuration] (2026-07-31)

Solution: Reconstructed root configuration by extrapolating from individual package configs. Fixed ssh-config 5.2 ESM/CJS interop issues by updating imports and test signatures.

Learnings

Domain Knowledge (domain)

• Nominal-only imitation learning is fundamentally unstable in closed-loop environments; even a small fraction (4%) of error-recovery trajectories can be the difference between functional and broken policies due to covariate shift.

Debugging (debugging)

• Checkpoint provenance verification via SHA256 hashing is definitive for confirming model state integrity, preventing wasted debugging time on ‘wrong model’ issues in distributed training.

Tools (tools)

• Automated configuration generation should use a ’thin adapter’ strategy (separating tool-specific notes from generic guidelines) to prevent documentation drift across multiple repositories.
• When root workspace config files are lost in pnpm monorepos, examining individual package.json ’extends’ fields provides a reliable method to reconstruct the structure accurately via static analysis.

Architecture (architecture)

• In distributed deep learning, missing environment variables are silent failure causes. Validation scripts must check configuration completeness before launch to avoid wasting GPU time.

AI Usage Notes

Effective Patterns:

• ✓ Using parallel AI agents to scan codebases and rebuild standard configuration files across many repositories simultaneously.
• ✓ Employing base64-encoded Python scripts sent via stdin over SSH to bypass complex cross-platform shell escaping issues between Windows and Linux.

Limitations:

• ✗ AI struggled to diagnose covariate shift from zero-success metrics without explicit human hypothesis guidance, defaulting to environmental checks.
• ✗ Claude Code’s security classifier prevents automatic execution of ‘gh pr merge’, requiring manual intervention for final merges despite technical capability.
• ✗ Automatically detecting missing module dependencies in remote environments often fails without prior knowledge of conda environment names.

Next Week Outlook

Next week priorities include increasing the ratio of recovery data in OpenVLA-LoRA training to mitigate covariate shift and improving closed-loop robustness. Continue monitoring the six concurrent training runs on Tianhe3 to completion, focusing on convergence metrics for coffee and stack tasks. Address remaining repository documentation inconsistencies and finalize the BetterSSH workspace stability after ESM updates.

Token Usage Statistics

Peak Day: 2026-07-27 — $80.45 / 106.1M tokens

Daily Average: $56.75