Starters of Meta Silent J1: A framework of certified reading of language models judging in harmony with the grounds and small data

Language models are now used for assessment activities and judgments, extend more than their traditional passage of text. This has led to “llm-Aaa-Jigh,” when models are checking out from other language models. Such assessment is essential for strengthening the learning pipes, the bench test, and program alignment. These men leaning from the internal imagination of the Chain-of-thought, showing human judgment procedures. Unlike the general memory models provided for specific scores, these models invade your thoughts, making them ready for the solutions such as mathematical problems, thinking and meaning. Their power of interpretation and verify the answers to the language and domains of default and disability in the language of the language model.

However, the current judgment of AI face problems with irregularity. Many rely on basic metrics or static, adequate inscriptions or open tests. The standard problem is a spavation Siinis, where the order of answers affect the final decision, compromising justice. Also, collecting a man-made data on a scale is expensive and time-consuming, reducing the generality of these models.

Several methods of existence have experienced these challenges, but the effective effect. Sylabanner and Deepseek-GRM depends on information included for people or strong training plans, reducing a variety of work environment. Others, such as deepseek-R1, depending on the test of large models but mistreated in strange rising. Static strategies and reserves for the internet connected to prevent powerful thinking, while new ways uses school formatting or released release indicates a minor improvement. Despite the biggest information and models, the working benefits of traditional programs have increased.

Investigators from Genai and good groups are imported J1 to deal with the above ends. J1 trains judging models for the framework based on learning, making them learn about certified signs. The team used generous data to create high quality and low-quality responses to instant tasks, which convert the guaranteed items for guaranteed learning judgments. The performance dataset includes 22,000 these are used to train two types of J1: J1-Llama-8b including J1-Llama-70BCompleted from lllama-3.1-8B-3.3-70B-Grate Mace Models, respectively. Models are trained using the Group Actitimization Optimization (GRPO), reinforced algorithm that eliminates the need for criticized models and accelerates the meeting.

In the case of training plan is the highest increase, where both (X, a, b) and (x, b, a) installation formats used in training format. Also, rewards based on conversion is used only when the model brings the right devings to all orders for both orders. This structure allows the judge to be fair and honestly reliable or answer the order. The framework supports a variety of variations: Models can release the last qualifications, number numbers for each response, or both. Various differences are included, checking only single answers using scores from 0 to 10. These components make UJ1 make a variable and effective system that can judge various functions.

The results obtained using the J1 models reflect the development of major performance of existing programs. Benxmarks widely used (Ty) on the contrary, the models such as Deentieseek-GRM-27B and Alainplanner-Llama-70b points 67.6%, respectively. Even a small J1-Llama-8B model passes the systems on the foundation of the Aloplanner-Llama-8B, 62.2% comparison. J1 also showed higher performance on other critical benches such as Airbiesbeen, RM-Bench, Judge This Development is not only written but important training data used in the X1.

Several hints from the study in J1:

• J1 training is used in 22,000 pairs of service delivery, including 17k from Wildchat and 5K from mathematical activities.
• Training uses GRPO, redesigning RL by avoiding the need for different critics of critics.
• Introducing a higher-level position, reducing the position of Siinias with rewards based on measuring.
• Two types of primary model, J1-Llama-8B and J1-Llama-Llama-70B, are trained for modest data but higher models.
• J1-Llamas-70B has received 69.6% points on PPE, passing Deepseek-GRM-27B (67.2%).
• It supports multiple judgment formats: writing and decisions, logging in scores, and seven scores.
• Models exceeded models organized from Deepseek-R1 and Openai's O1-Mini for several activities.
• Displays that factual consultation, not just the size of the data, it is important to accurate judgments.
• J1's framework makes it a regular judge working with guaranteed and uncertain activities.

In conclusion, the J1 routes basically basically re-plan how the structure is trained and tested. Data generation and consumption of learning exceeds traditional need for expensive species while promoting accurate, logical, and unchangeable. This work indicates that the judgment driven by information can disrupt the largest dependent models in data volume and standard alignment strategies. It also guarantees the idea that judicial models should be preoccupied first, and then pretend. By living work and often passes State-the-art programs, J1 sets a new sign in LLM-AsaaaaaA-Aaaaaaa-General training program.

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