| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - chemistry |
| - physics |
| - math |
| - biology |
| - science |
| pretty_name: open-rl |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| --- |
| |
| # Open-RL |
|
|
| [](https://opensource.org/licenses/MIT) |
| [](https://turing.com) |
|
|
| --- |
|
|
| ## Dataset Summary |
|
|
| This dataset contains **self-contained, verifiable, and unambiguous STEM reasoning problems** across Physics, Mathematics, Biology, and Chemistry. |
|
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| Each problem: |
|
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| * Requires multi-step reasoning |
| * Involves symbolic manipulation and/or numerical computation |
| * Has a deterministic, objectively verifiable final answer |
|
|
| The problems were evaluated against contemporary large language models. Observed pass rates indicate that the tasks are **non-trivial yet solvable**, placing them within reach of advanced models while still exposing meaningful reasoning gaps. |
|
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| This makes the dataset particularly suitable for: |
|
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| * Reinforcement learning (RL) fine-tuning |
| * Reward modeling |
| * Outcome-supervised training |
| * Verifiable reasoning benchmarks |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| | Field | Type | Description | |
| | ----------------- | ------ | ----------------------------------------- | |
| | `conversation_id` | string | Unique identifier for each QA pair. | |
| | `domain` | string | Physics, Math, Chemistry, Biology. | |
| | `sub_domain` | string | Specific discipline. | |
| | `question` | string | STEM problem statement (LaTeX supported). | |
| | `answer` | string | Deterministic ground-truth solution. | |
|
|
| --- |
|
|
| ## Example |
|
|
| ```json |
| { |
| "conversation_id": "217998", |
| "domain": "Physics", |
| "sub_domain": "Astrophysics", |
| "question": "Consider a Navarro–Frenk–White (NFW) dark matter halo profile where...", |
| "answer": "\( \frac{4GM_{0}}{r_{0}} + \frac{16\pi Gk}{r_{0}}\left[ \ln\left(\frac{r_{0}}{r_{s}}\right) + 0.31 \right] \)" |
| } |
| ``` |
|
|
| --- |
|
|
| ## Verifiability and Automatic Grading |
|
|
| A core design principle of this dataset is **objective verifiability**. |
|
|
| Each problem is constructed such that: |
|
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| * The final answer is deterministic |
| * Correctness can be evaluated programmatically |
| * No subjective interpretation is required |
| * There is a clear separation between reasoning steps and final outcome |
|
|
| ### Answer Types |
|
|
| The dataset includes answers that are: |
|
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| * Closed-form symbolic expressions |
| * Numerical scalars |
| * Algebraic identities |
| * Simplified analytic forms |
| * Canonical LaTeX representations |
|
|
| Because answers are deterministic, evaluation can be performed via: |
|
|
| * Exact string matching (after normalization) |
| * Symbolic equivalence checking (e.g., SymPy) |
| * Numerical tolerance comparison |
| * Unit consistency validation (where applicable) |
|
|
| --- |
|
|
| ## Data Quality Assurance Process |
|
|
| To ensure scientific validity of the answer, all tasks are prepared and reviewed twice by PhD experts. |
|
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| Key quality rubrics include: |
|
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| * Prompt and answer accuracy |
| * Clarity of prompt and underlying reasoning |
| * Expert-verified model breaking cases due to model’s incorrect reasoning process |
| * Google-proof originality validation. |
|
|
| --- |
|
|
| ## Reinforcement Learning and Outcome Supervision |
|
|
| This dataset is designed to support **outcome-based reinforcement learning** for reasoning models. |
|
|
| In contrast to preference-based RL (RLHF), which relies on subjective ranking signals, this dataset enables: |
|
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| * Outcome-supervised reinforcement learning (OSRL) |
| * Deterministic reward assignment |
| * Binary or graded correctness rewards |
| * Scalable automated evaluation |
|
|
| ### Example RL Setup |
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|
| Given: |
|
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| * Prompt: `question` |
| * Model output: predicted final answer |
|
|
| Reward can be computed as: |
|
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| * `+1` if the final answer matches ground truth |
| * `0` or `-1` otherwise |
| * Optional partial credit via symbolic or numerical closeness |
|
|
| This allows: |
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| * Policy gradient methods (e.g., PPO) |
| * Direct optimization against correctness signals |
| * Reward model bootstrapping |
| * Iterative self-improvement pipelines |
|
|
| ### Calibration Regime |
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| The problems were stress-tested against advanced language models and found to be: |
|
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| * Not trivially solved |
| * Not universally failed |
| * Within the capability frontier of modern LLMs |
|
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| This places them in a **learning-efficient regime**: |
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| * Hard enough to produce gradient signal |
| * Solvable enough to avoid reward sparsity |
| * Suitable for curriculum-style training |
|
|
| --- |
|
|
|
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| ## Future Directions: NuRL and Structured Nudging |
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| We plan to extend this dataset with additional problem sets and a structured **"nudge" augmentation layer** inspired by the paper *["Nudging the Boundaries of LLM Reasoning"](https://arxiv.org/html/2509.25666v1)*. |
|
|
| ### Motivation |
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|
| Standard online RL algorithms (e.g., GRPO-style approaches) can only learn from problems where the model occasionally produces correct rollouts. For sufficiently difficult problems with a **0% pass rate**, no reward signal is generated, and therefore no gradient updates occur. As a result, such problems cannot contribute to expanding the model’s reasoning frontier. |
|
|
| ### NuRL-Style Nudging |
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| To address this limitation, future versions of this dataset will include: |
|
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| * Abstract, high-level **hints ("nudges")** |
| * Hints generated conditionally using the gold answer |
| * Carefully designed cues that reduce problem difficulty without revealing the solution |
|
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| Under a NuRL-style training pipeline: |
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| 1. Rollouts are first generated without hints. |
| 2. If pass rate > 0%, standard RL proceeds. |
| 3. If pass rate = 0%, a structured hint is injected. |
| 4. A new batch of trajectories is generated with the hint. |
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| This enables: |
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| * Previously unsolvable samples to produce non-zero rewards |
| * Learning signal from frontier-level problems |
| * Expansion of the model’s upper reasoning bound |
|
|
| ### Design Principles for Effective Nudges |
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| Planned nudges will follow empirical findings from prior work: |
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| * Hints should be **abstract and knowledge-oriented**, not answer-revealing |
| * Hints should preserve distributional alignment with base policy reasoning |
| * Hints should be injected only when necessary |
| * Nudges are most effective after base RL convergence |
|
|
| --- |
|
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| This evolution positions the dataset not only as a verifiable benchmark, but as a controlled testbed for **upper-bound expansion in reinforcement learning for reasoning models**. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{turing_2026_open_rl, |
| title = {Open-RL }, |
| author = {Saurabh Patil, Anshuman Lall, Marko Pavlovic , Chinmayee Shukla, Seetesh Pande, Tejass Mohan Ukarde , Amanda Gollo Bertollo, Mahesh Joshi, Kihwan Han}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/TuringEnterprises/Open-RL} |
| } |
| ``` |
