Strategic Partnership Development Advisor

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Impact of data quality: The performance of is affected by the quality of the initial reasoning chain. Interpretation fidelity: The reasoning chain it generates may not fully reflect the internal reasoning process of the LLM, and there is also the problem of interpretation fidelity. 5) Similarities between is and reinforcement learning goals Iterative updating: Both reinforcement learning and reinforcement learning use iterative methods to update the model and continuously optimize its performance. Reward signal: generatesReward signal: generatesReward signal: generatesinference chain iteratively and uses the correct answer as a feedback signal, similar to the reward signal in reinforcement learning, to guide the direction of model updates.



6) Difference between and Reinforcement brazil email list Learning Objectives Objective Function: The objective function of is not exactly the same as the policy gradient objective in reinforcement learning. focuses more on generating and optimizing inference chains. Model Structure: uses pre-trained LLM models, while reinforcement learning can usedifferent types of models. Training method: uses a gradient-based method to update the model, while reinforcement learning can use different training methods, such as K-learning, Sarsa, etc. . Microsoft's r: Mutual Reasoning Makes Smaller LLMs Stronger at Solving Problems ) Main Contributions r is an innovative self-playing self-inference method designed to improve the inference capabilities of small language models (SLMs) without the need for fine-tuning or support for advanced models.



The basic idea is to decompose the reasoning process into two stages: generation and identification, and realize mutual learning between SLMs through self-playing. ) Key Innovation Highlights Rich reasoning actions: r introduces five human-like reasoning actions to simulate human behavior during the reasoning process, which allows the SLM to generate higher-quality candidate reasoning paths and efficiently explore the solution space. Mutually consistent discrimination: r uses another SLM with similar capabilities to the target SLM as a discriminator to evaluate the generated candidate reasoning paths. The discriminator helps the target SLM choose a more reliable reasoning path by completing some of the reasoning steps and providing feedback.