Format Dependency in Evaluation

Oct 13, 2025 by vinooalluri.com 35 views

Introduction

The evaluation methodology for pre-RL models, such as the Qwen2.5-1.5B model in <GRPO_From_Scratch_Multi_GPU_DataParallel_Qwen_2_5_1_5B_Instruct.ipynb>, is crucial in determining the accuracy and effectiveness of these models. However, there are potential limitations in the current evaluation methodology that may lead to inaccurate results. In this article, we will discuss these limitations and propose a solution to ensure that our metrics better reflect the intended capabilities of GRPO.

Format Dependency in Evaluation

The current rule-based answer extraction method heavily penalizes valid answers that do not strictly adhere to the specified XML-like format. For example, in <file>, the model's solution to problem <Ben has 8 apples more than Phillip does. Tom has three eighths as many apples at Ben has. If Phillip has 40 apples, how many apples does Tom have?> was incorrectly marked wrong despite being mathematically correct, solely due to formatting deviations. This format dependency in evaluation can lead to artificially low pre-RL baseline scores and overestimation of RL improvements.

Misalignment with GRPO Objectives

The original intent of DeepSeek R1's GRPO method is to enhance reasoning capabilities, not formatting compliance. However, the current evaluation conflates these two aspects, leading to:

Artificially low pre-RL baseline scores (~20% on GSM8K)
Overestimation of RL improvements (where gains may primarily reflect format learning rather than reasoning enhancement)

Proposed Solution

In PR https://github.com/aburkov/theLMbook/pull/12, I implemented a model-based answer extraction method that focuses on semantic correctness rather than strict format adherence. Experimental results show:

Pre-RL Qwen2.5 achieves ~70% accuracy on GSM8K (vs. original ~20%)
See detailed logs in the PR

Implications

The current methodology may significantly underestimate the baseline mathematical capability of the pre-RL model. Reported RL improvements might be skewed toward format compliance rather than genuine reasoning gains.

Conclusion

The evaluation methodology for pre-RL models is crucial in determining the accuracy and effectiveness of these models. However, there are potential limitations in the current evaluation methodology that may lead to inaccurate results. By implementing a model-based answer extraction method that focuses on semantic correctness rather than strict format adherence, we can ensure that our metrics better reflect the intended capabilities of GRPO.

Recommendations

Re-evaluate the current evaluation methodology: Consider the potential limitations and biases in the current evaluation methodology and re-evaluate the results.
Implement a model-based answer extraction method: Focus on semantic correctness rather than strict format adherence to ensure that the evaluation methodology accurately reflects the intended capabilities of GRPO.
Monitor and adjust the evaluation methodology: Continuously monitor the evaluation methodology and adjust it as needed to ensure that it accurately reflects the intended capabilities of GRPO.

Future Work

Further investigation into the limitations of the current evaluation methodology: Conduct a thorough investigation into the limitations and biases of the current evaluation methodology to ensure that it accurately reflects the intended capabilities of GRPO.
Development of a more robust evaluation methodology: Develop a more robust evaluation methodology that takes into account the potential limitations and biases of the current evaluation methodology.
Implementation of the proposed solution: Implement the proposed solution and monitor its effectiveness in ensuring that the evaluation methodology accurately reflects the intended capabilities of GRPO.

References

PR https://github.com/aburkov/theLMbook/pull/12
<GRPO_From_Scratch_Multi_GPU_DataParallel_Qwen_2_5_1_5B_Instruct.ipynb>
Q&A: Potential Inaccuracies in Pre-RL Model Evaluation Methodology ====================================================================

Q: What are the potential inaccuracies in the current evaluation methodology for pre-RL models?

A: The current evaluation methodology for pre-RL models may be inaccurate due to format dependency in evaluation, misalignment with GRPO objectives, and potential biases in the evaluation process.

Q: What is format dependency in evaluation?

A: Format dependency in evaluation refers to the current rule-based answer extraction method that heavily penalizes valid answers that do not strictly adhere to the specified XML-like format. This can lead to artificially low pre-RL baseline scores and overestimation of RL improvements.

Q: How does misalignment with GRPO objectives affect the evaluation methodology?

A: Misalignment with GRPO objectives occurs when the current evaluation conflates reasoning capabilities with formatting compliance. This can lead to artificially low pre-RL baseline scores and overestimation of RL improvements.

Q: What is the proposed solution to address the potential inaccuracies in the evaluation methodology?

A: The proposed solution is to implement a model-based answer extraction method that focuses on semantic correctness rather than strict format adherence. This approach has shown promising results, with pre-RL Qwen2.5 achieving ~70% accuracy on GSM8K.

Q: What are the implications of the current evaluation methodology?

A: The current methodology may significantly underestimate the baseline mathematical capability of the pre-RL model. Reported RL improvements might be skewed toward format compliance rather than genuine reasoning gains.

Q: How can we ensure that our metrics better reflect the intended capabilities of GRPO?

A: To ensure that our metrics better reflect the intended capabilities of GRPO, we can:

Re-evaluate the current evaluation methodology to consider potential limitations and biases.
Implement a model-based answer extraction method that focuses on semantic correctness rather than strict format adherence.
Monitor and adjust the evaluation methodology as needed to ensure that it accurately reflects the intended capabilities of GRPO.

Q: What are the next steps to address the potential inaccuracies in the evaluation methodology?

A: The next steps include:

Further investigation into the limitations of the current evaluation methodology.
Development of a more robust evaluation methodology that takes into account the potential limitations and biases of the current evaluation methodology.
Implementation of the proposed solution and monitoring its effectiveness in ensuring that the evaluation methodology accurately reflects the intended capabilities of GRPO.

Q: What are the benefits of implementing a model-based answer extraction method?

A: The benefits of implementing a model-based answer extraction method include:

Improved accuracy of pre-RL baseline scores.
Reduced overestimation of RL improvements.
Better reflection of the intended capabilities of GRPO.

Q: How can we ensure that the evaluation methodology is robust and accurate?

A: To ensure that the evaluation methodology is robust and accurate, we can:

Continuously monitor the evaluation methodology and adjust it as needed.
Consider multiple evaluation metrics to ensure that the results are comprehensive and accurate.
Engage with the community to gather feedback and insights on the evaluation methodology.