Open-RL

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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

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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.

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Example

{
  "conversation_id": "217998",
  "domain": "Physics",
  "sub-domain": "Astrophysics",
  "question": "Consider a Navarro–Frenk–White (NFW) dark matter halo profile where...",
  "answer": "\( \frac{4GM_{0}}{r_{0}} + \frac{16\pi Gk}{r_{0}}\left[ \ln\left(\frac{r_{0}}{r_{s}}\right) + 0.31 \right] \)"
}

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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)

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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

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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:

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.

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

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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.

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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/Turing-Open-Reasoning/}
}