justintiensmith/Reasoning_Benchmark_800

Reasoning Benchmark 800

Reasoning_Benchmark_800 is an 800-episode LeRobot v3 training dataset for studying semantic and compositional generalization in vision-language-action models. It combines four completed 200-episode datasets while preserving the original observations, actions, timing, and videos. Only the training-language metadata was reassigned.

The experimental control is that held semantic concepts are absent from the training prompts even though the corresponding visual and motor trajectories are present. Those trajectories receive an object-identity prompt or a more generic relation/action prompt. This makes held-prompt evaluation test semantic generalization rather than unseen motor behavior.

Composition

Combined episodes	Task family	Source dataset	Seen-language episodes	Masked/held-support episodes
0–199	Relational placement	SP_Relational_Placement_200	100 front/back	100 left/right trajectories prompted as next-to
200–399	Ordering/sequencing	SP_Sequencing_200	76 seen combinations	124 first-from-right/third-from-left trajectories prompted by object identity
400–599	Counting	SP_Counting_200	80 counts 0/2	120 counts 1/3 prompted as generic object transfers
600–799	Size recognition	MT_Size_Recognition_200	150 seen combinations	50 held object–size combinations prompted by object identity

This version intentionally excludes referential disambiguation and state recognition. They can be added in a later benchmark release after those source datasets are finalized.

Dataset summary

• 800 episodes
• 413,545 frames
• 30 FPS
• SO follower robot
• 5 RGB video observations: wrist, middle, above, right, and left
• 6-dimensional robot state and action
• 434 unique paraphrased training prompts
• 191 MP4 files, copied without re-encoding

Prompt assignment

The LeRobot task for each episode is taken only from the final prompt column of VLA Prompts.xlsx. The training prompts are represented conventionally by meta/tasks.parquet and the per-frame task_index column.

The benchmark_metadata/ directory contains:

• prompt_manifest.csv: compact episode-to-prompt mapping
• prompt_manifest.jsonl: full audit mapping, including workbook annotations
• prompt_manifest_summary.json: counts and manifest/workbook checksums
• source_datasets.json: exact source repositories, revisions, and offsets
• validation_report.json: completed integrity checks

The audit JSONL is provenance metadata, not a model input. Training pipelines should consume the standard LeRobot task field only. See benchmark_metadata/TRAINING_NOTES.md.

Validation

The merged artifact passed checks for:

• contiguous episode indices 0–799
• contiguous global frame indices 0–413,544
• contiguous per-episode frame indices
• exact episode prompt agreement with the 800-row manifest
• exact per-frame task-index agreement with each episode prompt
• preservation of every source episode's frame count
• SHA-256 multiset equality between all source and merged video files
• zero detected held-concept leakage in the assigned training prompts

Intended evaluation

Train each architecture on the same 800 episodes and prompt assignments, then evaluate with separate:

1. matched identity prompts,
2. seen semantic prompts, and
3. held semantic prompts.

Keep held evaluation instructions outside the training input pipeline.