Frugal-Thinking-30B-A3B: Easy Samples as Length Regularizers in Math RLVR

Paper: Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR

Project page: https://mbzuai-paris.github.io/Frugal-Thinking

Code is publicly available on Github.

Base Model: Qwen/Qwen3-30B-A3B-Thinking-2507

Authors: Abdelaziz Bounhar et al.

License: Apache 2.0

Overview

Frugal-Thinking-30B-A3B is a reasoning-optimized variant of Qwen3-30B-A3B-Thinking-2507 trained via Reinforcement Learning with Verifiable Rewards (RLVR) on the Frugal-Thinking dataset.

It introduces emergent brevity: the model learns to reason efficiently and generate concise, verifiable mathematical solutions—without any explicit length penalty. By retaining moderately easy problems during training, Frugal-Thinking implicitly regularizes reasoning length, reducing verbosity while preserving accuracy.

Training Setup

Training Stages

Performance Across Benchmarks

Evaluation metrics: Pass@1 (%) and Efficiency-Adjusted Accuracy

Max generation length: 42k tokens

Definition: Efficiency-Adjusted Accuracy (EAA)

To compare models jointly on accuracy and brevity, we introduce a new metric named Efficiency-Adjusted Accuracy (EAA). EAA penalizes unnecessarily long reasoning chains:

$\text{EAA}\gamma = a \times \exp!\left[-\gamma \cdot \frac{L - L{\min}}{L_{\max} - L_{\min}}\right]$

where a is accuracy, $L$ is average output length, and $γ$ controls how strongly long outputs are penalized ($γ$ = 3 in our experiments). Higher EAA means the model solves tasks efficiently, with fewer tokens for similar accuracy.

Results on Reasoning Benchmarks (42k-token decoding budget)

Format: pass@1 | EAA₃
(IFEval reports average accuracy instead of pass@1)

Average Reasoning Length

Conclusions

➡️ Frugal-Thinking-30B-A3B-Stage 2 outperforms all 4B-class baselines in both accuracy and efficiency, achieving similar performance to the 30B MoE model, and better on average.

➡️ ≈ 50–60 % reduction in reasoning length while preserving or improving performance.

Intended Use

This model is designed for reasoning-intensive workloads, with a particular focus on controllable efficiency and verifiability:

• Verifiable mathematical reasoning and competition-style problem solving (e.g., AIME, GSM, MATH, GPQA)
• Reasoning under constrained or variable test-time compute budgets
• Efficiency–accuracy trade-off analysis in RLHF / RLVR and test-time scaling studies
• Reasoning length control and compression, enabling shorter, more cost-efficient chains of thought
• Benchmarking reasoning robustness across context lengths and difficulty regimes
• Research on test-time compute allocation, adaptive reasoning, and frugal inference

⚠️ Not intended for (but could be used):

• Open-ended creative writing or stylistic generation
• Conversational agents requiring rich persona or emotional interaction

Citation

If you use this model, please cite:

@misc{bounhar2025frugalmath,
  title={Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR},
  author={Bounhar, Abdelaziz et al.},
  year={2025},
  journal={arXiv preprint arXiv:2511.01937}
}