\n ## Diagram: Reasoning Skill Selection Process ### Overview This diagram illustrates a process for selecting reasoning skills from an example bank, utilizing a Conditional Variational Auto-Encoder (CVAE) and a reasoning policy. The process involves pre-processing a query, encoding it with an off-the-shelf embedding model and a reasoning skill encoder, decoding to generate reasoning skills, and then selecting relevant examples based on a reasoning policy. The diagram is segmented into four main areas: Pre-Processing, Conditional Variational Auto-Encoder, and Selection. ### Components/Axes The diagram contains the following components: * **Pre-Processing:** Includes an "Example Bank", "Off-the-Shelf Embedding Model", and example question/answer pairs (Q & R). * **Conditional Variational Auto-Encoder:** Composed of a "Reasoning Skill Encoder", a "Decoder", and labeled with "z" (latent variable) and "R" (reasoning skill). * **Selection:** Includes a "Reasoning Policy", "Reasoning Skills", and "Selected Examples". * **Input Query:** A separate query example (Q & R) used for the selection process. * **Labels:** "Pre-Processing", "Conditional Variational Auto-Encoder", "Selection", "Q", "R", "z", "R". * **Arrows:** Indicate the flow of information between components. * **Shapes:** Rectangles represent processes/models, circles represent data points/skills, and a dashed line separates the CVAE from the Selection process. ### Detailed Analysis or Content Details The diagram details the following flow: 1. **Pre-Processing:** * An "Example Bank" (represented by a stack of books) provides examples. * A question "Q: Seven red apples and two green apples are in the basket. How many apples are in the basket?" is input. * The corresponding answer "R: We add 7 to 2 and get 9." is provided. * Both Q and R are fed into an "Off-the-Shelf Embedding Model" (yellow rectangle). 2. **Conditional Variational Auto-Encoder:** * The output of the embedding model (Q) is passed to a "Reasoning Skill Encoder" (yellow rectangle). * The encoder generates a latent variable "z". * "z" and "R" are fed into a "Decoder" (yellow rectangle). * The decoder outputs a set of "Reasoning Skills" (represented by purple dots within a circle). 3. **Selection:** * A new "Input Query" is presented: "Q: 2 toucans are sitting on a tree limb. 1 more toucan joins them. How many toucans in all?". * This query (Q) is also processed by the "Off-the-Shelf Embedding Model". * The output is fed into a "Reasoning Policy" (yellow rectangle). * The reasoning policy, along with the "Reasoning Skills" generated by the CVAE, selects "Selected Examples" (represented by red dots within a circle). * The selected examples are then fed back into the reasoning policy and the embedding model, creating a feedback loop. ### Key Observations * The CVAE is central to generating a diverse set of reasoning skills. * The reasoning policy acts as a filter, selecting the most relevant skills for a given query. * The feedback loop in the selection process suggests an iterative refinement of skill selection. * The diagram highlights the separation between the skill generation (CVAE) and the skill selection (Reasoning Policy). ### Interpretation The diagram illustrates a system for automated reasoning skill acquisition and application. The CVAE learns to represent reasoning skills in a latent space, allowing for the generation of diverse skills. The reasoning policy then leverages these skills to solve new problems by selecting the most appropriate ones. The feedback loop suggests a mechanism for adapting and improving the skill selection process over time. The use of an "Off-the-Shelf Embedding Model" indicates a reliance on pre-trained language models for representing the input queries and answers. The overall architecture suggests a modular approach to reasoning, where skill generation and selection are treated as separate but interconnected components. The diagram doesn't provide specific data or numerical values, but rather a conceptual framework for a reasoning system. It demonstrates a process for learning and applying reasoning skills, potentially enabling machines to solve complex problems that require more than just pattern recognition.