| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - question-answering |
| language: |
| - en |
| tags: |
| - astrodynamics |
| - astrophysics |
| - reasoning |
| - numeric-reasoning |
| - verifier |
| - rlvr |
| - spark-tested |
| pretty_name: Kepler astro-bench v0.1 |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: pool |
| data_files: pool.jsonl |
| - config_name: heldout |
| data_files: heldout.jsonl |
| --- |
| # Kepler astro-bench v0.1 |
|
|
| The benchmark behind **[Orionfold/Kepler-GGUF](https://huggingface.co/Orionfold/Kepler-GGUF)** — |
| a verifier-checked set of astrodynamics and quantitative-astrophysics word problems, each with a |
| single numeric gold answer and a **programmatic verifier that doubles as a reinforcement-learning |
| reward**. |
|
|
| ## What's here |
|
|
| | File | Rows | Purpose | |
| |---|---:|---| |
| | `pool.jsonl` | 120 | Training / selection pool — 16 formula families (9 orbital, 7 astrophysics), 3 difficulty tiers. | |
| | `heldout.jsonl` | 44 | External curveball held-out — different seeds + hand-curated edge cases, **disjoint** from the pool. The number on the model card is measured here. | |
| | `verifier.py` | — | `astro_numeric_match(...)` — the scorer. | |
| | `units.py` | — | SI-unit parsing/normalization used by the verifier. | |
|
|
| ## Row schema |
|
|
| ```json |
| { |
| "task_id": "astro-orb-leo_period-0000", |
| "topic": "orbital_mechanics", |
| "subtopic": "leo_period", |
| "tier": 2, |
| "prompt": "A satellite orbits at altitude h = 1,030 km ... Give your final answer as \\boxed{value unit}.", |
| "answer": "105.6 min", |
| "gold_value_si": 6336.46, |
| "gold_unit": "s", |
| "rel_tol": 0.02, |
| "hand_curated": false, |
| "params": {"h_km": 1030} |
| } |
| ``` |
|
|
| All physical constants are given **in the prompt** — the task tests reasoning, not memorization. |
| The expected answer is a single `\boxed{value unit}`. |
|
|
| ## The verifier *is* the reward |
|
|
| `astro_numeric_match` extracts the `\boxed{}` answer, normalizes units to SI, and checks the value |
| against the gold within a per-row relative tolerance (default ±2%). It returns a binary score, so it |
| plugs directly into an RLVR loop as the reward — the same scorer used to build Kepler's SFT corpus, |
| to gate the SFT checkpoint, and to run the head-to-head comparison. |
|
|
| ```python |
| from verifier import astro_numeric_match # needs units.py alongside |
| |
| reward = astro_numeric_match( |
| completion=model_output, # the model's full text, containing \boxed{...} |
| expected="105.6 min", # the row's "answer" field |
| rel_tolerance=0.02, # the row's "rel_tol" field |
| ) # -> 1.0 if correct within tolerance, else 0.0 |
| ``` |
|
|
| ## Known coverage gaps |
|
|
| Honest about its weak spots: the families `hohmann_transfer` (two-burn transfers) and |
| `altitude_from_period` (inverse Kepler) are the hardest rows and where models — including Kepler — |
| most often miss. Treat them as the frontier of this benchmark. |
|
|
| ## Methods |
|
|
| Full construction + measurement protocol: |
| [The Gate Before the GPU — Deciding SFT vs RL vs RLVR Before You Spend the Run](https://ainative.business/field-notes/the-gate-before-the-gpu/). |
|
|
| --- |
|
|
| Published by **Orionfold LLC** · [orionfold.com](https://orionfold.com) · Methods at [ainative.business/field-notes](https://ainative.business/field-notes/). |
|
|
