DATA_DICTIONARY.md

RLVRAMBench data dictionary

Static data protocol 1.0, evaluator 1.1. The separate admission protocol is specified in benchmark/decision/protocol.json; the memory-estimation comparison has its own benchmark/estimation/protocol.json. CSV files use UTF-8, a header row, and a blank cell for missing or unavailable information. Blank is not zero, success, or a memory failure. Measurements in MiB use 1 MiB = 1,048,576 bytes. Identifiers are opaque stable keys, not numerical features. All listed paths are relative to the extracted evidence archive.

Tables and joins

File in benchmark/	Unit	Key / relation
transfer_configurations.csv	One of 98 configurations used as a source or target	configuration_id; convenient settings/outcomes join for browsing
configurations.csv	One fixed setting within a study (212 rows)	configuration_id; no measured outcomes
outcomes.csv	Eligible and all-attempt summary for that setting	One-to-one with configurations.csv
configuration_results.csv	All 212 settings joined to outcomes	Browsing only, not blind candidate input
runs.csv	An eligible process (612 rows)	run_id; belongs to one configuration
attempts.csv	A recorded invocation (683 rows)	attempt_id; eligible invocations link to run_id
stage_measurements.csv	Externally sampled peak in an observed execution stage	(run_id, stage); 3,434 rows
queries.csv	A source-to-target prediction request (400 rows)	query_id; grouped by task_id
not_started_conditions.csv	A planned control condition that never started (6 rows)	Legacy condition/allocation record, not an attempt

The 612 eligible runs comprise the paper's original 588-process corpus and 24 completed batch-by-logging controls. The 683 attempts comprise 657 original attempts and 26 started controls. Sixteen historical source screens remain in the separate archival table profiles/standard_grpo/historical-temporal-source-trials.csv; they are not added to these totals. The archived factorial-phase-trials.csv is a different view of existing runs, not another experiment.

Configuration and case identity

configuration_id hashes the study, model, workload, condition, and explicit settings below, excluding outcomes, seeds, job IDs, and source revisions. case_id groups one study, model, workload, GPU count, and requested horizon. Keeping this grouping does not assert that historical runs used one immutable working copy. Raw environment and source records preserve that separate provenance limitation.

• study: boundary, state release, batch/reservation, original 40- or 100-step extension, sampling calibration, instrumentation control, horizon follow-up, four-GPU transfer, or batch-by-logging control.
• model_family: legacy short identifier; model_name is the readable checkpoint name; model_id identifies its upstream repository.
• workload_id, workload_name: stable identifier and readable name. legacy_dataset_id retains the original table spelling. In particular, code_heavy_tail is displayed as Longer-prompt code; this name does not assert a measured heavy-tailed distribution.
• algorithm: Group Relative Policy Optimization (GRPO). adaptation: low-rank adaptation (LoRA), training adapter matrices.
• configuration_level: test index or named experimental setting, not a throughput/quality rank. c0–c5 jointly change the actor micro-batch, generation-memory reservation, and actor parameter offload (enabled at c0–c1, disabled at c2–c5). Log-probability micro-batches remain fixed by workload: 8 for MATH and 4 otherwise. Actual values are in adjacent columns; do not infer them from the index. Control settings and interpolated horizon settings are not additional levels of the six-level grid.
• condition: experimental treatment, such as release/resident, external/full instrumentation, source/long horizon, or the batch/logging combination. Blank means that no separate condition field was recorded.
• used_in_transfer_tasks: 1 for the 98 scored-source/target configurations, 0 for supporting measurements. Supporting target-case controls are not permitted extra information for transfer prediction.

Explicit resource and workload settings

Column	Meaning and unit
gpu_count	Number of allocated graphics processing units
accelerator, device_capacity_mib	Device model and reported capacity per device
margin_limit_mib	Chosen per-device limit, 38,912 MiB, inclusive
peak_measurement	Measurement convention, maximum externally sampled per-device memory
rollout_tp_size	Number of devices sharing each generation model by tensor parallelism
actor_micro_batch	Prompt–response sequences processed together per GPU during actor update
rollout_logprob_micro_batch	Generation-policy log-probability micro-batch size
ref_logprob_micro_batch	Fixed-reference-policy log-probability micro-batch size
vllm_gpu_memory_utilization	Fraction reserved by the vLLM generation engine; not measured utilization
parameter_offload	Whether model parameters are offloaded from device memory
optimizer_offload	Whether optimizer state is offloaded
free_cache_engine	Whether generation state is released between stages
max_prompt_length	Maximum prompt tokens under the selected model's tokenizer
max_response_length	Maximum generated response tokens
max_model_len	Configured maximum total model sequence length, tokens
max_num_seqs	Maximum concurrent sequences configured for generation
rollout_n	Generated responses per prompt
train_batch_size	Global training batch size in prompts
train_max_samples, val_max_samples	Preparation/sample limits; not actual processed counts
total_training_steps	Requested training horizon
gpu_monitor_interval_ms	Requested external device-memory sampling interval, milliseconds
allocator_trace_enabled	Whether additional allocator instrumentation is enabled
allocator_trace_sync	Whether allocator instrumentation waits for pending GPU work
save_freq, test_freq	Configured checkpoint/validation intervals in steps; negative disables periodic events
val_before_train	Whether validation precedes training
resume_mode	Recorded checkpoint-resume setting
max_actor_ckpt_to_keep	Checkpoint-retention setting

Boolean settings retain True/False from the frozen matrices. A blank setting means it was not present in that source table, not that the option was disabled. Raw configuration and environment records remain authoritative for defaults. A final-step event may occur in addition to periodic events.

The measurements used the Karolina supercomputer at IT4Innovations in Ostrava, Czech Republic. They cover a particular colocated VERL/vLLM implementation on A100 devices; these columns do not specify a portable replacement for that execution stack.

Outcomes

The three output labels apply to three distinct eligible seed slots:

• within_margin: all three complete, each at or below 38,912 MiB.
• above_margin: all three complete, but at least one exceeds that limit.
• memory_failure: at least one eligible process has a diagnosed memory failure. Its sampled peak need not reach capacity before the failure.

not_repeated appears only for the twelve single-process sampling calibration settings. It is not a fourth prediction label; those settings never enter the transfer tasks.

eligible_processes, distinct_seeds, completed_eligible_processes, within_margin_processes, above_margin_processes, and memory_failure_processes are counts, not probabilities. max_completed_run_peak_mib and mean_completed_run_peak_mib use only completed eligible processes. They are blank if none complete and must not be mistaken for the unobserved peak a failed process would have reached. recorded_attempts, documented_completed_attempts, and excluded_attempts describe the full recorded history. label_scope identifies eligible resource outcomes or single-process calibration.

A slot can receive an eligible replacement after a documented exclusion. This does not erase the earlier attempt or turn its cause into an out-of-memory diagnosis. For the original 100-step extension, twelve attempts complete out of fourteen, whereas all twelve selected eligible outcomes complete within margin. Two excluded attempts stop after 63 and 77 steps; one reports a CUDA invalid argument and the other has unresolved weight-synchronization nonprogress.

Runs, attempts, and stages

• experiment_id identifies a planned configuration/seed slot; job_id identifies its scheduler allocation. Several processes can share an allocation, so a job ID alone is not a run ID.
• training_seed, pair_id, period preserve random-seed and paired-order information. Do not split those repeats into training/test examples.
• Run observed_state is the single-process resource result; completed_requested_work is 0/1 from the validated analysis, not from the legacy stage counter alone. Some completed calibration logs have a stage counter stuck at zero.
• requested_training_steps, elapsed_seconds, observed_peak_mib, and completed_run_peak_mib give the requested horizon, elapsed seconds, sampled peak, and peak only if the run completed.
• terminal_stage identifies the last observed stage; failure_kind retains the diagnosed failure category. A blank failure kind is not evidence that an excluded attempt succeeded or had a known non-memory cause.
• source_table names the source CSV. artifact_path, run_log, phase_memory_csv, gpu_telemetry_csv, environment_json, and allocation_manifest locate the corresponding archive evidence. source_head is the source revision when provided by the source table.
• Attempt eligible is 0/1; recorded_outcome and exclusion_reason retain the audit's wording. documented_completion=0 means completion is not established, not a newly diagnosed failure. evidence_files is a semicolon-separated list and scheduler_evidence points to additional scheduler evidence when available.
• last_completed_step_if_documented is populated for the two late excluded 100-step attempts from their execution logs; blank elsewhere means not curated here, not step zero.
• Stage stage is initializing, rollout (generation), reference_logprob (reference-policy scoring), actor_update (training), weight_sync (weight transfer), validation, checkpoint, idle, or unknown.
• Stage coverage is completed_run, terminal_stage_partial, or stage_completion_not_certified. The last two distinguish failure records from complete-run measurements. They do not certify completion of an earlier stage; consult the traces for a stage-specific contrast. All stage peaks here are external device-memory measurements, not PyTorch allocated-memory peaks.

The six never-started rows retain the control inventory's fields: experiment_id, pair_id, condition, period, model_family, dataset, training_seed, job_id, initial_allocation, attempted, outcome, observed_peak_mib, actor_update_peak_mib, and terminal_sampled_phase. attempted=0 means they are not included in any attempt denominator.

Queries, predictions, and metrics

query_id is the prediction key. task_id groups requests that share an allowed donor case; track gives the transfer type. source_configuration_id and target_configuration_id join to settings and outcomes; source_case_id and target_case_id make the separation explicit. Each row is a comparison, not a new experimental configuration.

Prediction CSVs have exactly one row per selected query and columns query_id,predicted_state. The evaluator rejects missing, duplicate, unexpected, or invalid predictions. Approval means predicting within_margin; the other two labels reject the target.

The score JSON reports correct three-state counts/accuracy, distinct target counts, approvals, approved memory failures, approved margin violations, and rejected within-margin targets. Each error is shown against its relevant target-state count; approval errors are also shown against the approved count. The confusion matrix has predicted rows and observed columns. A rate with zero denominator is JSON null, never an artificial zero error. Per-task and per-track results accompany descriptive pooled counts. Macro task accuracy weights tasks equally within a track.

Source costs count distinct source configurations, eligible processes, and recorded attempts supplied within each reported scope. They describe available evidence, not measured use by a rule or the cost of acquiring extra measurements. Shared donors mean that task costs must not be summed to estimate the whole release's cost.

within_margin_recall divides approved within-margin targets by all within-margin targets. within_margin_approval_precision divides those correct approvals by all approvals. Both retain repeated query weighting within the static task being scored. Neither is a probability of success on future jobs. Read them together with memory-failure and margin-violation approvals, not as interchangeable single-number rankings. reference_scores.json contains source-copy, always-approve and always-reject results.

Budgeted admission panel

This is a separate experiment, not another set of rows counted in the 612-run static tables. decision/protocol.json defines four cases and twelve candidates; decision/matrix.csv fixes 48 planned process slots. Each candidate has one queryable screening seed and three independent evaluation seeds. All four processes request the same five-step schedule. The three evaluation outcomes alone determine its repeated label.

File in benchmark/decision/	Meaning
protocol.json	Frozen rules, budgets, source evidence, cases and seed roles
matrix.csv	Complete execution settings for the 48 slots
amendment-01.json	Prelaunch evaluator-audit correction; original freeze retained
submission.json	Execution-source version and scheduler submission record
results/candidate_results.csv	Human-readable settings, source/screen/evaluation states, peaks and costs for each candidate
results/attempts.csv, results/attempts.json	Every started invocation, including unresolved outcomes
results/allocations.csv	Finalized scheduler allocations, including any without a started process
results/validated_runs.csv	Processes passing raw-evidence and provenance validation
results/stage_measurements.csv	Observed external stage peaks and sampled durations
results/realized_work.csv	Logged work and response-length summaries
results/scores.json	Per-case and pooled replay, acquisition transcripts, independent evaluation labels and sensitivity bounds
results/summary.csv	One rule–budget row; recall averages the four case-level recalls, while counts sum across cases
results/collection_summary.json	Slot, attempt, outcome and physical collection accounting
results/manifest.json	SHA-256 digests of the reconstructed result files

Attempt state is within_margin, above_margin, memory_failure, or unresolved. Unresolved is missing resource information, not a fourth successful prediction label. known_memory_failure preserves a diagnosed failure even if another validation check prevents an eligible label. completed_final_operations certifies the full requested schedule only for validated completions. validation_error explains any failed check; it must not be silently discarded.

Candidate screen_state uses seed 141. evaluation_state requires the three separate seeds 142–144. If any evaluation slot is unavailable, the aggregate remains unresolved, with known failure/above-margin evidence constraining its admissible outcomes. evaluation_known_memory_failure and evaluation_known_above_margin can both be true; these are overlapping evidence flags, not disjoint state counts.

The replay's approve, reject, and abstain are decisions, not the three resource labels. Requested screens are charged before revelation. Acquisition records never contain unrequested target outcomes. Supplied donor attempts, actually requested target attempts, and full physical collection costs have separate ledgers. Physical GPU-seconds multiply each recorded runner invocation's elapsed seconds by its two allocated GPUs; this excludes queue time and other allocation overhead.

Memory-estimation comparison

This comparison does not change the historical table counts above. The retrospective evaluation reuses ninety historical configurations, holding out an entire model family at a time. The separate prospective inventory fixes twelve larger-model configurations, three seeds per configuration, and eighteen matched two-/four-GPU pairs. A planned slot is not necessarily a launched process or an eligible memory measurement.

File in benchmark/estimation/	Meaning
protocol.json, amendment-01.json	Frozen design and prelaunch correction
model_metadata.json	Architecture and parameter counts derived from upstream configuration and tensor headers
matrix.csv, targets.csv	Execution slots and the twelve configuration-level prediction targets
retrospective.json	Separate family-held-out fits, predictions, scores and source-evidence costs
fitted_model.json, predictions.json	Final historical-only fit and predictions sealed before prospective execution
prediction_freeze.json	Timestamp and hashes binding the frozen inputs and outputs
execution_commits.json, execution_history.json	Immutable execution source for each pair and disclosed runtime repairs
results/configurations_flat.csv	Browsable settings, seed coverage, observed state and all three frozen predictions
results/processes.csv	All 36 planned slots, including validation failures and unknown launch status
results/configurations.csv	Configuration-level outcomes with structured settings and seed lists
results/pairs.csv	Two-/four-GPU outcomes in each matched allocation
results/evaluation.json	Resolved-subset scores, full-panel coverage and unscored approvals
results/costs.json	Task, invocation-reservation, observed-allocation and whole-allocation time
results/summary.json, results/provenance.json	Counts and independently checked evidence/source hashes
results/output_manifest.json	Hashes and sizes of the twelve reconstructed JSON/CSV outputs

The flat table is a browsing view, not a permitted input to blind prediction: it deliberately displays target outcomes beside predictions. gpu_count is the number of devices assigned to the training process; each enclosing allocation reserves four devices. eligible_seed_count, eligible_seeds and unresolved_seeds expose the coverage behind observed_state. All three seeds must be eligible before a repeated label can be assigned. Otherwise observed_state is unresolved, even when an eligible seed already establishes a failure. known_memory_failure and known_margin_exceedance retain such partial eligible evidence.

completed_peak_mib is available only when all three seeds complete with validated measurements. maximum_known_completed_peak_mib may summarize fewer completions; it must not be substituted for the completed repeated outcome. Host-memory exhaustion, software failure and missing evidence do not establish a GPU-memory label.

Each method has a <method>_predicted_state column. The method names are donor_copy, component_regression and logistic. Component regression supplies an estimated <method>_predicted_peak_mib; donor copying retains the donor's measured peak, not a newly estimated target peak. The classifier's blank peak field is not a zero estimate. An <method>_unscored_approval marks a predicted within-margin setting whose repeated outcome remains unresolved. Those approvals must accompany any reported precision or failure count on the resolved subset.

The process table separates launched, eligible, completed, online_status and reconstructed state. The runner's online status is not accepted as a label without checking its raw evidence. Unknown launch status is distinct from False. validation_errors explains unresolved measurements. Pair peak and runtime differences are reported only when both members have validated completions; they describe total execution under each device assignment, not identical generated responses.

The four cost views are alternatives, not additive charges: task time weights each invocation by its two or four assigned GPUs; invocation-reservation time weights both by four; observed allocation time includes recorded overhead through runner finalization; whole-job time requires actual scheduler start/end accounting. Each reports known and unknown record counts. A missing terminal record is unknown cost, not zero cost. Preflight jobs are reported separately in execution history.

The enhanced allocation_accounting.json preserves outer-job and batch-step records separately, with the original UTC scheduler lines, their hashes, one-second timestamp precision, and links to the recorded pair claims. A cancelled job's batch cleanup can end later than its outer record. Both measured spans remain explicit; they are never summed. A nonlaunched slot has no invocation cost, but its enclosing allocation can still have a verified scheduler cost.

Expanded-host follow-up

benchmark/host_capacity/ uses the same thirteen-file results schema as the estimation panel, but contains eighteen new planned slots, nine pairs and six configurations. It is a separate experiment, not a repair that replaces original outcomes. The fixed seeds are 161–163. The raw records are under output/estimation_host_capacity/.

study_freeze.json seals this new design and its inherited predictions. Provenance keeps its study_freeze_sha256 separate from the original prediction_seal_sha256. The new protocol_sha256 identifies the expanded host-resource condition; the original protocol and prediction seal remain additional anchors. No predictions are fitted from this panel.

All completion, partial-evidence and scoring definitions above still apply. In particular, exit code zero without evidence of the requested training step does not establish completion. Its sampled peak is not a completed-run peak. The per-panel cost files exclude each other's allocations.