Daily Report — 2026-07-28
Daily Overview
- What was done: Diagnosed critical failure modes in OpenVLA-LoRA policies trained on Nominal-only data and executed comprehensive evaluations across Coffee and Stack Three tasks.
- How it was done: Employed paired rollout comparisons, weight delta verification, and remote GPU orchestration (Tianhe3) to isolate covariate shift; developed automated JSON validation scripts for parallel benchmarking integrity.
- Impact: Proved that low training loss does not guarantee closed-loop success due to compounding errors on off-manifold states, establishing Recovery-augmented data as essential for robustness and validating the evaluation infrastructure.
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.
Tasks
Architecture & Strategy
- ✅ Coffee Nominal Zero Success Root Cause Analysis — Investigated 0% success rate in Coffee Nominal policy, ruling out pipeline bugs and identifying closed-loop covariate shift as the primary failure mode.
- ✅ OpenVLA-LoRA Training & Stack Three Evaluation Infrastructure — Initiated parallel training for six Robosuite tasks; merged and deployed Stack Three Nominal/Recovery (step-7000) checkpoints, launching dual evaluation servers on Tianhe3.
Implementation & Fixes
- ✅ Automated Validation & Remote Environment Management — Developed Python scripts to validate JSON output integrity (scene counts, seed uniqueness) and resolved remote environment dependencies (PYTHONPATH, scipy, OpenGL) for headless server execution.
Problems & Solutions
Critical Issues
1. Coffee Nominal policy exhibited 0% success rate despite low training loss (~0.13), initially suspected to be pipeline bugs or checkpoint corruption.
Solution: Performed symmetric paired evaluation against Recovery data and verified weight deltas; identified ‘closed-loop covariate shift’ where tiny initial biases compound, pushing the model out of its training distribution. Confirmed nominal data lacked recovery examples for off-manifold drift.
Key Insight: Training loss minimization masks critical frame failures; exponential compounding of step-wise probability drops leads to catastrophic closed-loop failure even with seemingly good metrics.
General Issues
2. Remote evaluation scripts failed due to missing dependencies (PYTHONPATH, scipy) and non-diagnostic seed selection.
Solution: Explicitly configured PYTHONPATH for remote agents; bypassed heavy framework imports by directly loading vla_server.py; switched diagnostic seeds to ones where Recovery succeeds to isolate model differences.
Key Insight: Diagnostics must use seeds that isolate variables of interest; minimizing package initialization side-effects is crucial for efficient debugging in shared/remote environments.
Human vs AI Approaches
Strategic Level
Diagnosing Zero Success Rate & Failure Mode Identification
| Role | Approach |
|---|---|
| Human | User rejected surface-level hypotheses (corruption/logs) and strategically guided investigation toward loss curves and nominal vs. recovery comparisons, identifying the conceptual issue of covariate shift. |
| AI | AI initially focused on procedural checks (integrity, libraries) and failed to connect symptoms to causal mechanisms without human intervention; provided mathematical derivation of error compounding only after user prompting. |
Difference Analysis: Human demonstrated high-level strategic debugging by identifying the conceptual failure mode (distributional gap), while AI was limited to procedural fixes and required guidance to understand the physics of the failure.
Implementation Level
Resource Management & Task Orchestration
| Role | Approach |
|---|---|
| Human | User dynamically reshuffled GPU resources (e.g., moving Coffee eval to Stack Three) based on real-time experimental feedback and priority shifts. |
| AI | AI maintained static schedules or suggested standard queues, lacking the contextual awareness to adapt resource allocation without explicit instruction. |
Difference Analysis: Human exhibited adaptive orchestration driven by experimental outcomes; AI provided passive execution without evaluating the strategic value of dynamic reallocation.
AI Limitations
Critical Limitations
- AI initially lacked the contextual reasoning to diagnose covariate shift from zero-success metrics, defaulting to environmental/config errors until explicitly guided by human hypothesis.
General Limitations
- Automated scripts encountered noise due to client-side config path issues (PowerShell) and non-diagnostic seed selection, wasting initial diagnostic resources.
Learnings
Key Learnings
- Nominal-only imitation learning policies are fundamentally unstable in closed-loop environments due to compounding trajectory errors; robustness requires explicit error-state (Recovery) training or online correction.
Practical Learnings
- Paired evaluations (shared seeds/state) are critical for isolating model performance from environmental randomness; automated JSON integrity checks are essential for trustworthy parallel benchmarking.
Conversation Summaries
✅ Coffee Nominal Failure Diagnosis & Stack Three Infrastructure Setup 04:12:13.526 | claude_code, codex Conducted end-to-end investigation of Coffee Nominal policy’s 0% success rate, identifying closed-loop covariate shift and data deficiency as root causes. Simultaneously established evaluation infrastructure for Stack Three by merging checkpoints (step-7000) and launching parallel servers. Validated pipeline integrity through systematic elimination of technical bugs and mathematical analysis of error compounding.