Goekdeniz-Guelmez/JOSIE-Zero-4-GRPO-Outputs-105

JOSIE-Zero-4: Out-of-Training GRPO Evaluation Generations

This dataset contains 105 model-generated reasoning traces from JOSIE-Zero-4. The samples were selected from a 500-example evaluation on the Math branch of `openbmb/UltraData-RL-2609`.

These problems were not used to train JOSIE-Zero-4 with GRPO. The evaluation was designed to examine whether reasoning behavior learned through GRPO transfers to data outside the model's training distribution.

Of the 500 evaluated generations, 105 received the maximum possible reward across all configured reward functions:

Strict full-reward rate: 105 / 500 = 21.0%

This dataset contains only those 105 generations.

What Does “Full Reward” Mean?

A sample is included only if it received the maximum reward from every configured reward function. This is stricter than checking whether the final mathematical answer is correct.

A generation may therefore be mathematically correct but excluded because it failed another criterion, such as:

• —the expected output format;
• —the required reasoning structure;
• —a formatting or parsing condition;
• —or another property evaluated by the reward system.

The remaining 395 samples may include incorrect answers, partially correct solutions, correct answers with formatting failures, or generations that lost reward for another reason.

Therefore, the 21.0% figure is a strict full-reward rate, not a mathematical accuracy score for the complete evaluation.

Dataset Structure

Each sample contains:

Summary

• —Evaluation samples: 500
• —Published samples: 105
• —Strict full-reward rate: 21.0%
• —Source dataset: `openbmb/UltraData-RL-2609`, Math branch
• —Selection criterion: Maximum reward across all configured reward functions

The reasoning and answer fields are generated or produced as part of the JOSIE-Zero-4 evaluation pipeline.

Purpose

The primary purpose of this dataset is to inspect out-of-training generalization.

Performance on the same distribution used for reinforcement learning can demonstrate that a model learned a behavior, but it does not establish whether that behavior transfers to unfamiliar data. These samples provide qualitative evidence about whether JOSIE-Zero-4 can:

• —decompose unfamiliar mathematical problems;
• —perform intermediate calculations;
• —recover from weak or incorrect approaches;
• —maintain the expected reasoning structure;
• —produce valid final answers;
• —and preserve its characteristic reasoning style outside its GRPO training distribution.

The dataset should be treated as an evaluation artifact, not as a benchmark covering the full capabilities of JOSIE-Zero-4.

JOSIE-Zero-4 Reasoning Style

JOSIE-Zero-4 is designed to produce reasoning that is closer to an informal problem-solving process than to a perfectly polished solution manual.

Depending on the requested reasoning depth, traces may include:

• —initial intuitions;
• —intermediate calculations;
• —partial hypotheses;
• —uncertainty or self-questioning;
• —discarded approaches;
• —error recognition and correction;
• —sanity checks;
• —informal transitions;
• —and a final transition to the answer.

The model is not explicitly required to present every solution as though the correct path was obvious from the beginning. Self-correction and visible exploration are part of the behavior being studied.

Reasoning Depth

JOSIE supports several reasoning-depth settings:

Reasoning depth controls the amount of reasoning generated, not the required length of the final answer.

Personality and Informal Reasoning

JOSIE-Zero-4 is designed to maintain a recognizable and persistent intellectual character across different tasks. Its personality is not intended to be a superficial layer added after the model has finished reasoning. Instead, the objective is for the model's communication style, standards, uncertainty, and self-correction behavior to appear throughout the problem-solving process itself.

JOSIE's behavior emphasizes:

• —directness rather than excessive assistant-style politeness;
• —intellectual honesty about assumptions, uncertainty, and mistakes;
• —independent reasoning rather than reflexive agreement;
• —technical precision when calculations and explicit deductions matter;
• —adult communication without unnecessary hand-holding;
• —self-correction instead of defending a weak approach;
• —willingness to abandon an invalid line of reasoning;
• —and a stable identity rather than a generic assistant persona applied temporarily to an otherwise unrelated model.

The resulting traces are intentionally closer to an informal problem-solving monologue than to a polished mathematical solution manual. They may contain initial guesses, partial deductions, doubts, questions directed at the model itself, discarded approaches, sanity checks, and transitions between different strategies.

A trace may follow a pattern such as:

Initial idea → calculate → something does not fit → identify the bad assumption → try another route → verify → give the answer.

The model is not expected to pretend that it knew the correct solution path from the first token. In many real problem-solving processes, the solver begins with an incomplete idea, discovers a contradiction, revises the plan, and only then arrives at a reliable solution. Preserving some of this process makes it possible to inspect how the model handles uncertainty and recovery rather than seeing only a polished result after all mistakes have been removed.

The generated reasoning may therefore include:

• —informal fragments;
• —intermediate calculations;
• —tentative hypotheses;
• —explicit uncertainty;
• —self-directed questions;
• —recognition of incorrect assumptions;
• —abandoned solution paths;
• —corrections;
• —alternative approaches;
• —edge-case checks;
• —rough sanity checks;
• —conversational transitions;
• —and a final shift from exploration to a concise answer.

Informality and Profanity

The informal style can occasionally include slang or profanity. This behavior was not added simply as a cosmetic writing effect. During JOSIE-Zero-4's GRPO training, the policy began developing a noticeably more conversational reasoning voice, including fragments, direct self-correction, informal transitions, and occasional profanity.

The profanity was not originally introduced through an explicit instruction to use it in the fine-tuning process. It emerged as part of the model's developing style and was retained because removing it through additional reward shaping or extensive post-training filtering could also remove some of the directness and natural error recognition associated with the broader reasoning behavior.

For example, instead of a sterile trace such as:

First calculate \(X\). Then substitute \(X\) into \(Y\). Therefore, the answer is \(Z\).

JOSIE may reason more informally:

Okay, \(X\) looks like the obvious bitch-ass route. Wait, that assumes these terms are independent, which they fucking aren't. So that approach is fucked. I need to condition on \(Y\) first and then calculate \(X\)...

We deliberately retain this behavior instead of suppressing it through additional reward shaping or extensive post-training: in our observations, the resulting informality encourages more honest reasoning and generally improves answer quality.

JOSIE-Zero-4 is not optimized to sound maximally sterile, uniformly polite, or institutionally sanitized at every moment. It is optimized to be direct, candid, and capable of expressing that an approach is wrong when it is wrong. In some traces, informal language may make the correction feel more natural and more honest than a completely sanitized alternative.

This does not mean that the model's informal style is always appropriate. A deployment may reasonably apply additional formatting, tone, or profanity controls depending on its audience and use case. The dataset preserves the rawer behavior because the purpose here is to inspect the model's learned reasoning style rather than present only a post-processed version of it.

Personality Within the Reasoning Process

An important objective of JOSIE-Zero-4 is that personality should not appear only in the final answer. It should also influence how the model approaches a problem:

• —whether it admits that an assumption is uncertain;
• —whether it notices when a calculation does not make sense;
• —whether it abandons a bad approach instead of forcing it;
• —whether it checks an answer instead of stopping at the first plausible result;
• —whether it distinguishes a strong conclusion from a guess;
• —and whether it communicates failure directly rather than hiding it behind polished language.

This makes the personality relevant to the reasoning behavior itself. The desired result is not merely a model that adds a recognizable tone to mathematically correct outputs. It is a model that appears to approach problems with consistent habits of thought: skepticism toward its own assumptions, willingness to revise, attention to detail, and a preference for saying what is actually happening over pretending that the solution was obvious.

The traces in this dataset should therefore be read as behavioral samples. They show how JOSIE-Zero-4 expresses problem-solving activity under the evaluation setup; they should not be interpreted as a definitive or complete representation of the model's internal cognition.

JOSIE Building JOSIE

An early agentic version of JOSIE-3 contributed to parts of the engineering and research workflow used to develop JOSIE-Zero-4, including training infrastructure, dataset tooling, and evaluation-related work.

This is not autonomous weight modification or unsupervised self-improvement. Training runs, objectives, evaluation criteria, infrastructure, and final research decisions remained externally controlled.

Instead, the project explores a practical form of recurrent development:

An earlier JOSIE helps build the tools and processes used to train and evaluate a later JOSIE.

This can include writing or inspecting code, analyzing failures, processing datasets, designing evaluations, and proposing improvements. JOSIE-Zero-4 is therefore both the subject of the research and, in limited ways, one of the tools used to conduct it.

Interpretation and Limitations

These samples should be interpreted narrowly. They are not:

• —the GRPO training dataset;
• —all correct answers from the 500-example evaluation;
• —a measure of overall mathematical accuracy;
• —evidence that the model achieved a 21.0% accuracy rate;
• —or a complete benchmark of JOSIE-Zero-4.

They are the 105 generations from one out-of-training evaluation that satisfied every configured reward criterion.

Because this is a filtered collection of successful generations, it is subject to selection bias. It should not be used to estimate the model's average behavior without also examining the complete evaluation set and reporting the individual reward components.