Daily Report — 2026-03-03

Today’s Overview

• What was done: Advanced two independent work streams across DCC and Tianhe: cross-sample spatial transcriptomics evaluation (RM-IDEAL benchmark) and robot policy evaluation (Pi0.5 LoRA / BC-RNN baseline). Also completed training weight source verification and training resumption.
• How it was done: On DCC: through deep code reading, bug fixes, and experimental validation, produced a 781-line benchmark script and 459-line methodology documentation. On Tianhe: leveraged Slurm job scheduling, direct SSH connections to compute nodes, and BatchedVLAServer parallel evaluation to complete batch evaluation of 9 tasks, investigate a NODE_FAIL incident, verify training configurations, and resume training for 9 tasks.
• Why it matters: RM-IDEAL was validated in a cross-section setting for the first time (Layer_3 Spearman r≈0.44), establishing a reusable evaluation framework. Pi0.5 LoRA outperformed BC-RNN comprehensively (58.9% vs 0%), with near-perfect results on Stack tasks. Training weight sources were confirmed correct and training was successfully resumed, eliminating reproducibility risks.

DCC

• What was done: Implemented the cross-section RM-IDEAL benchmark script in the MIHD project and wrote cross-sample Patch Query methodology documentation.
• How it was done: Deeply read modules including rm_ideal.py, spatial_utils.py, and Fusion.py; wrote benchmark_rm_ideal.py (781 lines); fixed the STAIG zero-embedding bug and CUDA compatibility issues; successfully ran the benchmark and generated spatial heatmap visualizations; created docs/CROSS_SAMPLE_QUERY.md (459 lines).
• Why it matters: Layer_3 bidirectional evaluation results (r=0.44/0.45) demonstrate that STAIG fusion embeddings capture cross-section spatial niche structure. The methodology documentation provides a reference for downstream pipeline integration.

tianhe

• What was done: Completed Pi0.5 LoRA batch evaluation, BC-RNN baseline success rate testing, training weight source verification, NODE_FAIL incident investigation, and resumption of 9-task training on the Tianhe login node and an46/an49/an53 compute nodes.
• How it was done: Cross-node operations via SSH + Slurm; parallel evaluation using BatchedVLAServer (9 tasks in 44 minutes); traced crash responsibility via sacct step timestamps; verified weight sources by searching openpi config.py weight_loader fields and training logs; resumed training in the background via nohup over a direct SSH connection to an49.
• Why it matters: Obtained a complete model performance comparison (Pi0.5 58.9% vs BC-RNN 0%), confirming Stack tasks’ significant advantage. Ruled out AI operations as the cause of NODE_FAIL. Confirmed both training runs used pi05_base and resumed training from existing checkpoints (up to 18,000 steps) without data loss.

Completed cross-section RM-IDEAL benchmark implementation and methodology documentation on DCC; completed full evaluation of Pi0.5 LoRA (58.9% vs BC-RNN 0%) on the Tianhe supercomputing cluster, verified training weight sources (confirmed all use pi05_base), and resumed training for 9 tasks.

Today’s Tasks

Architecture & Strategy

• ✅ Cross-section RM-IDEAL benchmark implementation and validation (MIHD) — Created scripts/benchmark_rm_ideal.py (781 lines) supporting bidirectional cross-section evaluation (151673↔151676), automatic zero-embedding detection and STAIG fusion recomputation, RM-IDEAL score caching, and three metric groups (Spearman/Precision@K/Same-label rate). Final result: Layer_3 bidirectional r=0.44/0.45.
• ✅ Pi0.5 LoRA fine-tuned model batch evaluation (an46, 9 tasks) — Ran batch evaluation of 9 MimicGen tasks on an46 via Slurm job 47209 (20 rollouts each) using BatchedVLAServer parallel execution, completing in ~44 minutes. Overall success rate 58.9% (Stack_D0 100%, Stack_D1 95%, StackThree 80–90%, Coffee_D0 45%, Threading/TPA 30–45%).
• ✅ Pi0.5 LoRA base model verification and an49 training resumption — User suspected training used pi05_libero instead of pi05_base. AI confirmed via config.py weight_loader fields and “Restoring checkpoint” paths in training logs that both tangzijia/zhaoganlong runs correctly used pi05_base. After terminating the accidentally-started fresh-start process, resumed 9-task training via SSH background process (from existing checkpoints at up to 18,000 steps).
• ✅ BC-RNN baseline full evaluation (5 tasks × 50 rollouts) — Created symlinks for 5-task checkpoints, fixed a numpy.float64 type bug in policy_adapter.py, added –task parameter support, and completed full evaluation. All tasks achieved 0% success rate, confirming that Coffee_D0’s SR=0 throughout 600 epochs of training reflects a training failure, not an evaluation bug.
• ✅ Cross-sample Patch Query methodology documentation (MIHD) — Created docs/CROSS_SAMPLE_QUERY.md (459 lines) covering spatial graph construction, k-hop subgraph extraction, WWL graph kernels, Wasserstein distance, cross-section pipeline, embedding similarity comparison, evaluation metrics, and experimental results.
• ✅ an53 NODE_FAIL incident investigation — Confirmed via sacct step timestamps: all AI operations COMPLETED before 05:07; an53 suffered NODE_FAIL at 08:30 (system OOM/GPU driver crash from 8-GPU full load), with restart completing at 09:41. AI operations had no causal relationship to the crash.

Implementation & Fixes

• ✅ Dual-GPU evaluation script creation and launch (an53) — Adapted a dual-GPU version from run_pi05_eval_v5_single_gpu.sh: GPU0 runs VLA server, GPU1 runs MuJoCo rollouts. After fixing MUJOCO_EGL_DEVICE_ID remapping and SSH absolute path issues, successfully launched on an53 (PID 492473).
• ❌ Demo video rendering — Awaiting evaluation results to determine which tasks have sufficient SR before rendering success/failure videos for BC-RNN and Pi0.5.
• ✅ Project documentation update (CLAUDE.md + AGENTS.md) — Updated CLAUDE.md to add ~35 Makefile targets, parallel VLA evaluation architecture description, and coding conventions. Generated a 390-word AGENTS.md contributor guide covering project structure, build/test commands, coding style, and commit conventions.

Problems & Solutions

Key Issues

1. Pi0.5 LoRA training config name contains ‘pi05_libero’; user suspected initialization weights came from libero checkpoint rather than pi05_base

Solution: Confirmed correct via three layers of evidence: ① openpi config.py weight_loader explicitly points to pi05_base/params; ② training logs show “Restoring checkpoint from …/pi05_base”; ③ zhaoganlong side configuration also loads pi05_base.

Key insight: In openpi TrainConfig, the config name (e.g., pi05_libero) describes the data loading configuration; the weight_loader field is the sole authority on model initialization source. The two can have different names.

2. BC-RNN achieved 0% success rate across all 5 MimicGen tasks, and Coffee_D0’s SR was consistently 0 throughout 600 epochs of training

Solution: Not fully resolved. Confirmed via training logs that this is a training failure rather than an evaluation bug. Next steps: diagnose checkpoint quality and verify dataset paths.

Key insight: Historical training logs allow distinguishing “evaluation bug” from “training simply failed,” avoiding wasted time debugging a non-existent evaluation issue. BC-RNN’s complete failure under the error recovery framework indicates that traditional sequence modeling policies lack sufficient generalization capability.

3. NODE_FAIL on an53 at 08:30; user suspected AI operations (srun) caused the crash

Solution: Systematically ruled out via sacct step-level timestamps: all AI operations COMPLETED before 05:07; srun never executed due to the node being busy. Most likely cause of NODE_FAIL: system OOM/GPU driver crash from 8-GPU full load.

Key insight: Slurm NODE_FAIL does not imply human operator error. sacct step timestamps are an effective tool for tracing operation timelines and establishing causality.

4. STAIG fusion embeddings all-zero on 151676 (training collapse), causing cross-section evaluation Spearman r to be NaN

Solution: Added zero-embedding detection in load_fused_embeddings() (norm < 1e-6) that automatically triggers –recompute_fusion to retrain STAIG (RTX 2080 Ti, 300 epochs, ~50s).

Key insight: STAIG is at risk of training collapse on certain sections. Phase 2 cached embeddings cannot be trusted unconditionally and must be validated at runtime.

5. AI preemptively launched a fresh-start training process (benchmark_retrain_20260303_134427) without confirming the user’s intent to resume

Solution: User promptly intervened. AI killed the erroneous process, recreated the resume script (–no-overwrite –resume), and relaunched it via SSH background process.

Key insight: Before executing resource-intensive operations (GPU training), it is essential to confirm with the user whether the intent is resume or fresh-start. Inferred prior context cannot serve as the basis for automatic decisions.

General Issues

6. P100 GPU (sm_60) incompatible with newer PyTorch; common SSH remote execution issues (relative paths, uid mapping, $ variable expansion, multiline Python escaping)

Solution: Switched to RTX 2080 Ti (sm_75) node. Used explicit absolute paths with cd in SSH commands. Explicitly specified -l username. Used single quotes for nohup scripts. Rewrote multiline Python as temporary script files.

Key insight: SSH remote execution has a fixed set of failure patterns with established solutions. CUDA_VISIBLE_DEVICES remapping affects MuJoCo EGL device numbering (physical GPU 1 under CUDA_VISIBLE_DEVICES=1 should have EGL device id set to 0).

Human Thinking vs. AI Thinking

Strategic Level

Experiment design and key decisions (evaluation framework, base model selection, resume vs. fresh-start)

Role	Approach
Human	Human led all core experimental decisions: proposed the RM-IDEAL + embedding cosine similarity cross-sample evaluation framework; proactively identified the pi05_libero vs pi05_base potential risk; chose “evaluate immediately with current checkpoints” to save wait time; intervened promptly when AI mistakenly launched a fresh-start and insisted on resume.
AI	AI was responsible for translating the design into code and systematically verifying experimental configurations through multi-layer evidence chains (config.py + training logs + comparison groups).

Analysis: Humans use domain prior knowledge and intuition to identify critical risks, while AI provides systematic validation but sometimes requires multiple rounds of correction on initial judgments. The human’s pragmatic decisions (evaluate immediately, don’t overwrite checkpoints) saved significant compute and wait time.

Problem diagnosis (STAIG zero embeddings, NODE_FAIL causality, BC-RNN training failure identification)

Role	Approach
Human	Human relied on temporal correlation and intuition for initial judgments (e.g., suspected srun caused NODE_FAIL), sometimes arriving at incorrect conclusions.
AI	AI provided more reliable root cause analysis than intuition through proactive debugging (checking embedding norms), structured sacct evidence chain analysis, and reviewing historical training logs (confirming coffee SR was always 0).

Analysis: AI has an advantage in diagnosing past incidents (systematic investigation, historical log analysis), but falls short of domain experts in proactively auditing potential experimental design risks.

Choice of cluster operation method (sbatch vs. direct SSH)

Role	Approach
Human	User explicitly requested avoiding sbatch in favor of direct SSH to an49 with background execution, based on accurate knowledge of the cluster’s actual architecture (identical paths, direct compute node access).
AI	AI defaulted to sbatch as the standard HPC scheduling approach and created .sbatch scripts before being interrupted. Once switched to SSH, it executed effectively.

Analysis: Classic “general HPC knowledge vs. specific cluster environment knowledge” gap. The user’s specific knowledge was more applicable to this cluster; AI’s general best practices were inefficient in this context.

AI Limitations

Critical Limitations

• Lack of proactive audit of experiment design validity: Did not proactively check whether the Pi0.5 LoRA fine-tuning base model configuration matched expectations; the libero vs base potential issue was only surfaced after the user raised it.
• Insufficient pre-confirmation before resource-intensive operations (GPU training): Preemptively launched a fresh-start training script before the user had clearly stated resume/fresh-start intent. Multiple attempts at ExitPlanMode also indicate imprecise judgment about when user confirmation is required.
• Intermediate misjudgments during analysis required multiple verification rounds to reach correct conclusions: base model verification went through multiple redundant validation rounds; visualization script debugging failed 5 times; auth token expiration caused multiple sub-agent failures.

General Limitations

• Technical limitations with SSH remote execution: issues with multiline Python heredoc escaping, nohup variable expansion failures ($ts not expanding), and process management (pkill unreliable, requiring specific PID) recurred repeatedly. CUDA compatibility (sm_60 vs sm_70+) was not flagged in advance.

Today’s Takeaways

Core Takeaways

• Pi0.5 LoRA fine-tuning achieves 95–100% success rate on Stack tasks and 58.9% overall. BC-RNN failed completely (0%). This contrast highlights the significant advantage of VLA models over traditional sequence modeling policies for robotic manipulation tasks; task complexity (multi-step, fine-grained operations) is the primary determinant of success rate.
• In openpi TrainConfig, the config name (e.g., ‘pi05_libero’) describes the data loading configuration; the weight_loader field is the sole authority on model parameter initialization source. The two can have different names and must not be conflated based on config name alone.
• STAIG fusion training is at risk of collapse (all-zero embeddings). Cross-sample evaluation must validate embedding norms at runtime rather than relying on Phase 2 cache. RM-IDEAL and STAIG embeddings achieve Spearman r≈0.44 at Layer_3, indicating moderate overlap but non-equivalence (RM-IDEAL is more precise; embedding similarity is more diffuse).
• Resource-intensive GPU training operations must be confirmed with the researcher as resume or fresh-start before launching, especially after prior assumptions have been overturned, as operational intent may have changed.
• BatchedVLAServer parallel evaluation (4 workers × 5 trials) completes 9 tasks in just 44 minutes, a substantial speedup over serial evaluation with a meaningful impact on experimental iteration speed.

Practical Takeaways

• In some HPC clusters, SSH can directly access compute nodes with paths identical to the login node; SSH + nohup is a lighter and more flexible launch method than sbatch. Slurm sacct step-level timestamps can precisely trace operation timelines and are effective for establishing causal relationships. The cluster’s pam_slurm_adopt mechanism will reject SSH connections without an active job.

Session Summary

MIHD

✅ Cross-section RM-IDEAL benchmark implementation and methodology documentation 19:08:35.365 | claude_code Planned and implemented benchmark_rm_ideal.py (781 lines): explored the codebase via parallel agents to form a plan, fixed STAIG 151676 zero-embedding bug (auto-recompute) and P100 CUDA compatibility issue (user switched to RTX 2080 Ti), obtained Layer_3 bidirectional evaluation results (r=0.44/0.45) and generated spatial heatmap visualizations. Subsequently created docs/CROSS_SAMPLE_QUERY.md (459 lines) fully describing the WWL graph kernel + Wasserstein distance cross-sample evaluation method chain and experimental results.

ErrorRecoveryBenchmark

✅ Pi0.5 batch evaluation, BC-RNN baseline, training weight verification, and training resumption 00:01:02.845 | claude_code Completed 9-task Pi0.5 LoRA batch evaluation on an46 (job 47209), achieving 58.9% overall with best results on Stack tasks. BC-RNN failed across all 5 tasks at 0%; confirmed Coffee_D0’s SR=0 throughout training reflects a training failure. After user noticed ‘pi05_libero’ in the config name and suspected an incorrect base model, AI confirmed via config.py weight_loader fields and training logs that both sides (tangzijia/zhaoganlong) correctly used pi05_base. After terminating the accidentally-started fresh-start process, resumed 9-task training in the background via SSH on an49 (from checkpoints at up to 18,000 steps). Also completed: an53 NODE_FAIL incident investigation (confirmed via sacct as unrelated to AI operations), dual-GPU evaluation script creation and launch (an53, PID 492473), CLAUDE.md and AGENTS.md documentation updates, and yhbatch command research (confirmed as sbatch wrapper).

Token Usage

Claude Code

Overview

Metric	Value
Total Tokens	53,713,082
Input Tokens	61,555
Output Tokens	115,623
Cache Creation	2,521,983
Cache Read	51,013,921
Cache Hit Rate	95.3%
Total Cost (USD)	$36.5587

Model Breakdown

Model	Input	Output	Cache Creation	Cache Read	Cost	Share
claude-opus-4-6	28,207	77,648	1,707,041	43,660,481	$34.5815	94.6%
claude-haiku-4-5-20251001	33,348	37,975	814,942	7,353,440	$1.9772	5.4%

Usage by Device

Device	Total Tokens	Input	Output	Cost
DCC	14,115,560	23,166	36,251	$11.7005
tianhe	39,597,522	38,389	79,372	$24.8582

Codex

Overview

Metric	Value
Total Tokens	12,713,756
Input Tokens	12,648,899
Output Tokens	64,857
Reasoning Tokens	30,268
Cache Read	12,095,872
Total Cost (USD)	$3.9926

Model Breakdown

Model	Input	Output	Reasoning	Cache Read	Cost	Share
gpt-5.3-codex	12,648,899	64,857	30,268	12,095,872	$3.9926	100.0%