Shanghai

Mathematical thinking is always a difficult place of artificial intelligence (AI) due to difficulty solving the problems and the need for a formal, logical thinking. While large models of languages ​​(llms) make great progress, they often struggle with activities that require a number of steps. Learning Strengthenance (RL) has shown to enhance skills, but traditional methods deal with challenges when start-up and binary rewards, provide this small response.

Shanghai Ai laboratory have been developed The Rewarding Reward Reward Reward (Oreal)series of math model models are available as OReal-7B and OReal-32b. This is a framework for conditions that only the rewards is the reward of the binary reward – the right or wrong – available. In contrast with familiar RL methods depending on dense response, the oreal uses Best-N (Bon) Bample of Code of Ethics and verify negative rewards to maintain consistency.

OReal-7B and OReal-32B indicates that small models can act in competitive models. Oreal-7b Access 94.0% Pass @ 1 Score on the Math-500 BenchmarkThe result if you compare with 32B models ago, while OReal-32B up to 95.0% Pass @ 1, passing past models are trained in water.

Technical Understanding and Benefits

OReal Framework introduces several key strategies to develop mathematical thinking:

1. The best sample of moral behavior: Sumpling Zon helps to properly select the right trajectories, allowing the model to learn from proper solutions.
2. Renewing Numbers: By changing bad rewards, the framework confirms the consistency of stems between fair and incorrect samples, desolating well.
3. The Koken-Lelgel Reward Model of Cack-Very Cack – Mathematical thinking often adds long-step sequence. Oreal provides importance for weights in key thought tokens, addressing the binary response.
4. RECORDING FOR POLICY ORGANIZATION: The model has refined itself according to organized questions, improving the efficiency of training and flexibility.

These strategies empower the stable training and better working on chronological work, making the strengthening of learning some practical approach.

Working and Assessment

Oreal models tested across several symbols:

• Math-500 Benchmark:
  • Oreal-7b up to 94.0% PASS @ 1The performance level previously seen in 32b models.
  • OReal-32B up to 95.0% PASS @ 1putting a new standard in mathematical thinking.
• AIED2024 and Olympikidbench:
  • Oreal models of Oral Modelfform Basenis Multiple, showing regular stability about the types of problems.
• Compare in Openaai O-Series and Deep Models:
  • OReal-32B passes Deepseek-R1-Pepill-Qwen-32b including Open Opena-O1-Previewshowing active training strategies.
  • Oreal-7b reaches parity results with Qwq-32b-view first including Open-o1-minito highlight the impact of its formulation of strengthening.

Store

Shanghai Ai Lab's OReal-7B and OReal-32b Models provide a refined method of strengthening learning mathematical thinking. Dealing with the challenge of binary binary rewards through The best sampling of n N, reward formation, and the importance of the Token LevelThese models achieve competitive performance even in small scales. OReal Framework provides important insight into the strengthening of complications of complex consultation activities, raising new indicators to solve the AIs in formal areas.

Survey paper, oreal-7b including ORUAL-32B. All credit for this study goes to research for this project. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 75k + ml subreddit.

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