Incorrect answers improve mathematical thinking? Strengthening to Learn by Certified Rewards of Certified Rewards (RLVR) Wonderful QWEN2.5-Math

With the NLP Languages ​​(NLP), RL Methods, such as the strengthening learning of the people's response (RLHF), used to improve the effects of the model by doing answers based on the reply ads. Different reading, certified Rewards, extends this method by using default signals, such as statistical accuracy or syntactic features, which enable a large response to language models. RLVR is especially interesting because it promises to develop thoughtful models' without needing a wide population. This defective response and consultation activities form an interesting study area, where developers aim to reveal how models can learn how to deal with math, logically, or orderly.

The persistent challenge in a mechanical learning of the construction models can consult effectively under less or incomplete monitoring. On activities such as resolving mathematical problems, where the appropriate response may not be immediately available, researchers have dealt with a mechanism for directing the model. Models often read from low-quality information, but it is impossible to enter a lot of information in full accurate accuracy, especially in consultation activities that need to understand the complexities such as proof or programming. As a result, there is an open feelings that models can learn when they are exposed to Nomobo, misleading, or incorrect signals during the training. This issue is important because the excessive memory models may not be able to chase properly when such support is not available, thus reducing the application of the real world conditions.

Fewer plans aim to develop reasoning skills by strengthening the strengthening (RL), with RLVR for the key focus. Traditionally, rlvr use “true” labels, the correct answers are verified by people or default tools, providing rewards during training. Other methods rest this requirement using many vaice votes or Heuristics are ready for formats, such as leaking responses that follow a specific style of the results. Some methods have tried with random rewards, giving beautiful signals without checking the accuracy of the answer. These methods aim to evaluate whether models can even read less guidance, but especially focus on certain models, such as QWEN, to raise concerns about different buildings.

Investigators from the University of Washington, Allen Institute for Ai, and UC Berkeley investigates the question by examining various reward signals in QWEN2.5-Math, the family of large languages ​​are well organized by mathematical thinking. They examine true rewards – rewards, many votes, rewards designed based on the reduced speech, random rewards, and wrong rewards. It is amazing that, they see that even completely critical symptoms, such as random rewards and rewards of incorrect answers, can result in great benefit in QWEN models. For example, the QWEN2.5-Math-7b Training on Math-500 with Trown-True Rewards expressed 28.8% improvement, while using the wrong labels and lead to 24.6%. Reward Rewards are still produced by enabling 21,4%, and format rewards led to 16.4% development. Most of the Voitity Rewards give the amount of the total accuracy of 26.5%. This was made up not limited to one model; QWEN2.5-Math-1.5B and indicate powerful benefits: Rewards Format increasing in 17.6% accuracy, and incorrect labels by 24,4%. However, similar rewards have failed to submit similar benefits in other strange families, such as LLAMA3 and OLMO2, indicating small or negative changes in training training. For example, Iloma3.1-8b saw the performance down to 8.5% under certain strange signs, highlighting a specific state of the ideal development.

The method of research team is involved using RLVR training in Finevir Models with these various rewards, restoring the need for authenticity of fact or random response. They have found that QWen models, or not available for appropriate responses, we are still able to learn to produce high-quality consultation results. The main understanding of QWEN models often show different behaviors called “Code Code”, Producing the organized statistical solutions such as code, especially in the Python format, regardless of a reward indicator. This code tendency was repeated in training, up from 66.7% to 90% in QWEN2.5-Math-7b when training is trained in strange rewards. Answers that include the Reasoning Code displays high numbers, usually about 64%, compared to 29% for the answers without thinking patterns. These patterns are consistently, suggesting that strange rewards can open recent recent skills during hypocrisy rather than new consultation skills.

Working data emphasizes amazing disability of QWEN models. Relief from random rewards (21.4% on Math-500) and incorrect labels (24.6%) are approximately compared to the benefits of true reward-28%. Similar trends appear in work, such as AMC, where the formatting, there, randomly productive rewards produced around 18%, is only low than 25 or most factors. Even in AIM2024, opposing rewards such as format (+ 13.0%), incorrect (+ 8.7%), and random (+ 6,3%) led to reasonable achievement, especially AIED2020202020202020202020202029 questions.

A few important ways from research includes:

• QWEN2.5-Math-7b receives 28.8% accurate rewards, but also 24 rewards.4% with incorrect rewards, 16.4% with format rewards, and 26.5% of format rewards.
• Code consultation patterns come from QWEN models, increased from 66.7% to 90% + under RLVR, confirming accuracy from 64% to 64%.
• Non-QWen models, such as Llama3 and Olmo2, did not show the same improvement, LLAMA3.1-8b dealing with up to 8.5 working at the governing rewards.
• Benefits from difficult signals appear within 50 measures of training in many cases, which raise as soon as possible for the thinking skills.
• This study warns that RLVR courses should avoid making ordinary results based on QWEN models only, as the opposition reward is not the universe.

In conclusion, these findings suggest that while qwen models can receive sensitive signs to improve performance, the same is not true in other important families. Non-qwen models, such as LLAMA3 and Olmo2, showed flat performance or negative changes in the training of sensitive signals. Research emphasizes the importance of verifying RLVR methods in various models rather than only in QWEN-Centric results, as many new documents have passed.

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