TLDR: Large language models (LLMs) often generate overly verbose solutions, increasing inference costs. This research introduces “frugal reasoning,” a method that retains and up-weights moderately easy problems during Reinforcement Learning with Verifiable Rewards (RLVR) training. This acts as an implicit length regularizer, teaching models to produce significantly shorter solutions (nearly half the length) while maintaining or improving accuracy on complex mathematical reasoning tasks like AIME25, thereby reducing verbosity and improving efficiency without explicit length penalties.
Large language models (LLMs) have made incredible strides in complex reasoning tasks, from solving advanced math problems to writing code. However, this progress often comes with a significant drawback: verbosity. These models tend to generate excessively long solutions, which increases the computational cost and time required for inference. A new research paper titled “Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR” introduces an innovative approach to tackle this issue, demonstrating that conciseness and performance can go hand-in-hand.
Traditionally, in Reinforcement Learning with Verifiable Rewards (RLVR) pipelines, a common practice is to filter out “easy” problems during training. The rationale is to maximize training efficiency by focusing on harder problems that demand longer reasoning chains. However, the authors, Abdelaziz Bounhar, Hadi Abdine, Evan Dufraisse, Ahmad Chamma, Amr Mohamed, Dani Bouch, Michalis Vazirgiannis, and Guokan Shang, argue that this approach inadvertently teaches models to equate “thinking longer” with “thinking better.” This skews the output length distribution, leading to models that produce unnecessarily verbose outputs, even when a shorter, more direct solution would suffice.
The core idea behind their “frugal reasoning” method is surprisingly simple yet effective: instead of discarding moderately easy problems, they propose retaining and modestly up-weighting them during training. They demonstrate that exposing the model to solvable tasks that require short reasoning chains acts as an implicit length regularizer. This process constrains the model’s output distribution, preventing it from becoming overly verbose without needing any explicit penalties for length.
The research outlines a two-stage training methodology. The first stage, termed “emergent brevity,” involves training on a dataset where moderately easy problems are intentionally given a higher proportion. These problems, with their stable positive rewards and concise reasoning traces, guide the model towards efficiency. The second stage, “Curriculum RLVR,” then builds upon this foundation, progressively introducing harder problems while maintaining the learned efficiency.
Experiments were conducted using the Qwen3-4B-Thinking-2507 model with a 16k token limit. The results were compelling: the Frugal-Math-4B models achieved baseline pass@1 accuracy on the AIME25 benchmark while generating solutions that were, on average, nearly twice as short. This significant reduction in output length translates directly into lower inference costs and faster processing. The study also introduced an “Efficiency-Adjusted Accuracy (EAA)” metric to jointly evaluate performance and conciseness, showing that their method drastically improved EAA compared to baseline models.
The training dynamics further supported their claims. During the initial training phase, the model’s average response length sharply decreased, and the rate of premature truncation (due to hitting the token limit) also declined. This indicated that the model was learning to complete its reasoning within the available context more efficiently. Importantly, this emergent brevity coincided with a steady increase in validation accuracy, proving that conciseness and correctness can improve simultaneously.
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The findings highlight that data curation plays a critical role in shaping the efficiency of reasoning models, not just reward design or model size. By carefully managing the training data to include and emphasize moderately easy problems, LLMs can learn to be both accurate and concise, leading to more practical and cost-effective AI applications. For more details, you can read the full research paper here.
