README.md

---
pretty_name: RLVRAMBench — Electronics submission, A100 and MI250X
license: mit
language:
- en
tags:
- benchmark
- reinforcement-learning
- gpu-memory
- reproducibility
- resource-configuration
- a100
- mi250x
task_categories:
- other
configs:
- config_name: mi250x_configurations
  data_files:
  - split: measured
    path: amd_mi250x/results/configurations.csv
  default: true
- config_name: mi250x_trials
  data_files:
  - split: measured
    path: amd_mi250x/results/trials.csv
- config_name: mi250x_attempt_ledger
  data_files:
  - split: measured
    path: amd_mi250x/results/attempt_ledger.csv
- config_name: mi250x_hardware_transfer
  data_files:
  - split: measured
    path: amd_mi250x/results/analysis/hardware_transfer_labels.csv
- config_name: mi250x_horizon_pairs
  data_files:
  - split: measured
    path: amd_mi250x/results/analysis/horizon_pairs.csv
- config_name: independent_controls
  data_files:
  - split: measured
    path: benchmark/offload_factorial/results/configurations.csv
- config_name: independent_control_runs
  data_files:
  - split: measured
    path: benchmark/offload_factorial/results/slots.csv
- config_name: independent_control_predictions
  data_files:
  - split: measured
    path: benchmark/offload_factorial/results/prediction_scores.csv
- config_name: matched_offload_changes
  data_files:
  - split: measured
    path: benchmark/offload_factorial/results/offload_pairs.csv
- config_name: a100_adaptation
  data_files:
  - split: measured
    path: profiles/electronics_revision/adaptation/outcomes.csv
- config_name: a100_reservation
  data_files:
  - split: measured
    path: profiles/electronics_revision/reservation/outcomes.csv
- config_name: a100_release
  data_files:
  - split: measured
    path: profiles/electronics_revision/release/outcomes.csv
- config_name: a100_iteration_cost
  data_files:
  - split: measured
    path: profiles/electronics_revision/timing/outcomes.csv
- config_name: llmem_native_pairs
  data_files:
  - split: measured
    path: profiles/electronics_revision/llmem_native/pairs.csv
- config_name: estimator_scores
  data_files:
  - split: measured
    path: estimator_comparison/results/score_comparison_all_methods.csv
- config_name: admission_candidates
  data_files:
  - split: test
    path: benchmark/decision/results/candidate_results.csv
- config_name: admission_attempts
  data_files:
  - split: recorded
    path: benchmark/decision/results/attempts.csv
- config_name: transfer_configurations
  data_files:
  - split: measured
    path: benchmark/transfer_configurations.csv
- config_name: all_configurations
  data_files:
  - split: measured
    path: benchmark/configuration_results.csv
- config_name: eligible_runs
  data_files:
  - split: measured
    path: benchmark/runs.csv
- config_name: recorded_attempts
  data_files:
  - split: recorded
    path: benchmark/attempts.csv
- config_name: stage_measurements
  data_files:
  - split: measured
    path: benchmark/stage_measurements.csv
- config_name: transfer_queries
  data_files:
  - split: evaluation
    path: benchmark/queries.csv
- config_name: larger_model_configurations
  data_files:
  - split: measured
    path: benchmark/estimation/results/configurations_flat.csv
- config_name: expanded_host_configurations
  data_files:
  - split: measured
    path: benchmark/host_capacity/results/configurations_flat.csv
- config_name: larger_model_replication
  data_files:
  - split: measured
    path: benchmark/completion_replication/results/configurations.csv
- config_name: randomized_control_runs
  data_files:
  - split: measured
    path: benchmark/replication_control/results/replication_control_trials.csv
- config_name: randomized_control_contrasts
  data_files:
  - split: measured
    path: benchmark/replication_control/results/replication_control_cases.csv
- config_name: configuration_feasibility
  data_files:
  - split: configurations
    path: tables/three-model-configurations.csv
---

# RLVRAMBench — Electronics submission

This is the dataset accompanying **Memory Feasibility in Colocated Language-Model
Reinforcement Learning: A Failure-Aware Measurement Study**, prepared for the
MDPI *Electronics* revision on **23 October 2026**.

**Submission version: `electronics-submission-2026-10-23-r2`.** This version is
published on `main` and pinned by the same tag. Dataset DOI:
[10.57967/hf/10780](https://doi.org/10.57967/hf/10780).
Earlier releases remain in the repository history and `releases/`.

- [Main manuscript](paper/RLVRAMBench_MDPI.pdf)
- [Scientific supplement](paper/RLVRAMBench_supplement.pdf)
- [Point-by-point reviewer response](paper/RLVRAMBench_reviewer_response.pdf)
- [Editable TeX source ZIP](paper/RLVRAMBench_Electronics_revision_source.zip)
- [Matching reproduction code](https://github.com/kobzaond/RLVRAMBench-reproduce/tree/electronics-submission-2026-10-23-r2)
- [Release manifest and archive hashes](release.json)
- [Submission data dictionary](SUBMISSION_DATA_DICTIONARY.md)
- [Reconstruction guide](SUBMISSION_REPRODUCTION.md)

Authors: Ondřej Kobza and Jan Šedivý, Czech Institute of Informatics,
Robotics and Cybernetics, Czech Technical University in Prague.
Author ORCIDs: [Kobza](https://orcid.org/0000-0002-0529-9860),
[Šedivý](https://orcid.org/0000-0003-0626-2303).

## What the dataset measures

RLVRAMBench measures GPU-memory feasibility when language-model generation
and reinforcement-learning updates share devices. It retains successful,
memory-failing, and ineligible executions. It provides stage measurements,
controlled changes to execution settings, repeated configuration labels,
frozen prediction tasks, and raw evidence. It does not measure model
intelligence, convergence, or full-training quality.

The main experiments use synchronous LoRA-GRPO with VERL/vLLM on A100-40GB
GPUs. Follow-ups add full-parameter 1.5B/3B actors, reservation and state-release
sweeps, iteration costs, native LLMem validation, and an MI250X replication.
The MI250X panel includes PPO with a full-parameter critic and changes
hardware **and** software together; it is not an isolated hardware comparison.

## Submission cohorts

The views above expose separate experimental units. Do not sum their rows
as independent experiments.

| Cohort | Coverage and interpretation |
|---|---|
| Historical transfer benchmark | 52 tasks, 400 queries reusing **94 distinct targets** |
| Independent A100 offload controls | 144 runs; 48 three-seed configurations: 36 within margin, 12 memory failures |
| A100 adaptation and no-KL controls | 108 formal runs: 106 within margin, 2 ineligible |
| A100 reservation sweep | 144 runs: 111 within margin, 11 above margin, 21 memory failures, 1 ineligible |
| A100 state-release sweep | 36 runs: 17 within margin, 6 above margin, 12 memory failures, 1 ineligible |
| A100 iteration costs | 24 runs, all within margin; 20 measured updates per run |
| Native LLMem validation | 24 prediction/measurement pairs in the supported native training setting |
| MI250X | 462 planned slots: 363 completions, 98 memory failures, 1 ineligible outcome; 154 configurations |
| DeepSpeed comparison | Recorded estimator calls and decisions on the same 400 benchmark queries |

The default view contains the 154 MI250X configurations, with `panel`
identifying the five substudies. Its A100 replay has **79 agreements among
89 resolved configurations, plus one unresolved configuration**.
The unresolved Phi-4-mini/GSM8K c5 setting still exposes its two diagnosed OOMs.
The MI250X ledger records 510 started jobs and 511 executions, including
one Slurm restart. Pilots and unstarted submissions are separately identified.

## Outcome rules and units

Memory is the maximum used memory on any one allocated device, in **MiB**,
not a sum across devices. Completed-run peaks require successful completion
and valid measurement evidence. An observed peak from a failed run is not
its counterfactual completed memory demand.

| Label | Meaning |
|---|---|
| `within_margin` | All three eligible repetitions complete below or at the operating limit |
| `above_margin` | All three complete; at least one exceeds the operating limit |
| `memory_failure` | All three repetitions are eligible; at least one has a diagnosed device-memory failure |
| `unresolved` | Fewer than three eligible repetitions; known adverse observations remain visible |

The operating limit is capacity minus **2,048 MiB**: 38,912 MiB for the
A100 devices and 63,472 MiB for MI250X GCDs. This is a declared operating
convention, not a probability guarantee. Per-run status vocabularies differ
between historical collectors; see the data dictionaries. Missing values
are missing evidence or inapplicable quantities, not zero memory.

## Load a view

The direct Hub-loading interface is tested with Python 3.11,
`datasets==5.1.0` and `huggingface_hub==2.2.0`. Install these from
`dataset-client-requirements.txt` in a separate reader environment. Older
clients such as `datasets==2.12.0` ignore the card's configuration mapping
and may try to combine unrelated audit JSON files. The frozen raw-analysis
environment is documented separately in the reproduction guide.

```sh
python -m pip install -r dataset-client-requirements.txt
```

```python
from datasets import load_dataset

configurations = load_dataset(
    "kobzaond/RLVRAMBench",
    "mi250x_configurations",
    revision="electronics-submission-2026-10-23-r2",
    split="measured",
)
```

Other views retain controls, historical transfer tasks, A100 follow-ups,
MI250X raw-derived trials, scheduler accounting, and estimator comparisons.
Use the configuration selector above or the inventory in
`verification/dataset-views.json`.

## Raw evidence and reproduction

All four required raw-evidence archives are available under `releases/`:

1. `RLVRAMBench-review-artifact-submission-20260917-b0018d0.tar.zst`:
   historical measurements, admission/estimation, and replications.
2. `RLVRAMBench-offload-raw-20261022.tar.zst`:
   independent offload controls, frozen source, and allocation accounting.
3. `electronics-full-20261021-a53af63.tar.zst`:
   the four A100 follow-up panels, development attempts, native LLMem,
   and their frozen execution sources.
4. `RLVRAMBench-mi250x-raw-20261022.tar.zst`:
   MI250X logs, device maps, memory/phase traces, submissions, and amendments.

Each archive has a SHA-256 sidecar. `release.json` records archive hashes,
coverage, and the code revision. `MANIFEST.sha256` covers the submission
files. The reproduction guide separates raw reconstruction from replay of
already derived tables. No new GPU experiments are needed to reproduce the
released analyses. Verification reports describe author-performed checks;
they do not claim external replication.

The `electronics_revision_2026_10/` folder is retained as an earlier curated
snapshot. The root-level paths and version tag above define this submission.
Historical IEEE PDFs remain archived; the linked MDPI PDFs are the current
submission documents.

## Evaluation contract and limitations

Transfer queries expose source evidence and target settings. Target outcomes
are public for audit but must not be read when producing predictions for
that task. Independent screening and evaluation seeds remain distinct.
Report failure approvals, above-margin approvals, useful approvals, and
coverage together; aggregate accuracy alone is insufficient.

Measurements depend on the specified stack, device capacity, model, workload,
state-release behavior, and requested duration. The longest runs cover 100
steps; the additional MI250X duration panel covers 40. The evidence does not
establish frontier-scale feasibility, multi-node scaling, behavior on other
engines or accelerators, or full-schedule convergence. The native LLMem
and out-of-scope DeepSpeed component comparisons have different purposes.
See the paper for scope and attribution limits.

## License and citation

Original benchmark code, measurements, and documentation use the MIT license.
Third-party material retains its original terms; see [RIGHTS.md](RIGHTS.md).
Model weights and optimizer checkpoints are excluded. Source datasets and
model identifiers are provenance, not a relicensing of upstream assets.

Use [CITATION.cff](CITATION.cff), the dataset DOI, and the version tag when
citing this release. The companion manuscript is a journal submission;
no acceptance or publication status is implied.