Datasets:

TuringEnterprises
/

Open-RL

license: mit
language:
  - en
tags:
  - chemistry
  - physics
  - math
  - biology
  - science
pretty_name: open-rl
size_categories:
  - n<1K
task_categories:
  - question-answering

Open-RL

Dataset Summary

This dataset contains self-contained, verifiable, and unambiguous STEM reasoning problems across Physics, Mathematics, Biology, and Chemistry.

Each problem:

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.

This makes the dataset particularly suitable for:

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

{
  "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:

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:

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.

Key quality rubrics include:

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:

Outcome-supervised reinforcement learning (OSRL)
Deterministic reward assignment
Binary or graded correctness rewards
Scalable automated evaluation

Example RL Setup

Given:

Prompt: question
Model output: predicted final answer

Reward can be computed as:

+1 if the final answer matches ground truth
0 or -1 otherwise
Optional partial credit via symbolic or numerical closeness

This allows:

Policy gradient methods (e.g., PPO)
Direct optimization against correctness signals
Reward model bootstrapping
Iterative self-improvement pipelines

Calibration Regime

The problems were stress-tested against advanced language models and found to be:

Not trivially solved
Not universally failed
Within the capability frontier of modern LLMs

This places them in a learning-efficient regime:

Hard enough to produce gradient signal
Solvable enough to avoid reward sparsity
Suitable for curriculum-style training

Future Directions: NuRL and Structured Nudging

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".

Motivation

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

To address this limitation, future versions of this dataset will include:

Abstract, high-level hints ("nudges")
Hints generated conditionally using the gold answer
Carefully designed cues that reduce problem difficulty without revealing the solution

Under a NuRL-style training pipeline:

Rollouts are first generated without hints.
If pass rate > 0%, standard RL proceeds.
If pass rate = 0%, a structured hint is injected.
A new batch of trajectories is generated with the hint.

This enables:

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

Planned nudges will follow empirical findings from prior work:

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

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

@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}
}