Microsoft and Biquant investigators launched Logic-RL: Legal framework that receives a consulting patterns such as training on logic puzzles

Large models of languages ​​(llms) developed most in their post-training paragraph, such as Deepseek-R1, to me – K1.5, and Opelai-O1, showing impressive thinking skills. While Deepseek-R1 provides a training model of the source model, the data code and data questions, raises questions about rating smaller models, well-educational data, and multiplication methods. Traditional mathematical datasets such as GSM8K and Omini-Math Attronsistent of various difficulty levels of various logical light, controlled processing. The need for refer-based information has condemned diversity and studying the appearance of the LLM.

The skills of the llMs consultation skills through various strategies, with the thought of chain-of-thought (cot) reflects an important role in decline in complex problems. Monte Carlo Tree Search (MCTS), successfully in the Alphagago, modified to coordinate the model based on testing and exploitation of tree and random sample. In addition, the learning strategies after enhancing the ability to consult with more readiness or reinforcement verification (RL) in special datasets. Methods such as the most popular DIPO (DPO), Proximal Optimization (PPO), PPO, PPO), and Emotion ++ shows the promise, creating a model of development model.

Investigators from Microsoft Research Asia, Ubiquincant, and independent proposed Logic-RL, RL frameworks from the law that receives a Deepseek-R1 for training on logical puzzles. Welcome to ++ algorithm and reward projects from Deepseek-R1 of the following training. As training continues, the natural model calls for additional consultation steps, extend from large production of the thousands of tokens, which enables a deep test. Using 5K-produced puzzles, their 7B model displays the general crossing, which improves 125% in AIME and 38% in AMC against the basic model. This suggests that RL-trained direction is developing problems solutions without composting the domain.

The investigators deal with the challenges with QWEN2.5-Math-7b inclination to produce Python blocks and collaborate with the requirements of formatting. Assessing both QWEN2.5-7b-Base and QWEN2.5-7b-leatment produces almost the same metric training metrics, including verification of verification, in response, and answering the curves. Start implementation shows a remarkable development in consultation skills, in length of discharge from the first tokens 500 to 2000 tokens that follow 1000 RL training steps after 1000 repairs. This enables the ability to appear in the more complex behavior, such as other solutions, and these ethical empires enhance the power of a complex function and adapted to the reported consequences to Deepseek-R1.

The results indicate that while PPO reached great benefits with accuracy and accuracy, it was slow to 138% rather than strengthening ++ at training speed. Emphasis ++ reflects high quality, work benefits, and efficient comparisons compared to the GRPO, passes through all Metrics. GRPO shows a weak functioning between three algorithms at RL algorithms. Super Oood model (of-Distribution) The usual power testifies usual, to achieve total 125% of AIMA data and 38% on the AMC Dataset. This harmonious development shows that the RL procedure improves distribution function and helps the appearance of consultation, delegated referral.

This study shows a larger logic-rl power to create difficult skills to consult with language models of RL-based RL. However, it is important to accept that the findings based on the small logic datagram, which can limit its performance. The general results of these major conditions of the country or codes are always a open question that requires another investigation. Future research should focus on finding this method in various and complex daspots to ensure its efficiency and feed of all domains and problems. By keeping this work as an open research project, researchers aim to benefit the broad scientific society.

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Sajjad Ansari final year less than qualifications from Iit Kharagpur. As a tech enthusiasm, he extends to practical AI applications that focus on the understanding of AI's technological impact and their true impacts on the world. Intending to specify the concepts of a complex AI clear and accessible manner.