Inputs and Rubric Classification Mechanism of RBRMs
A rule-based reward model (RBRM) processes three inputs: an optional prompt, the response output generated by the policy model, and a human-written rubric structured with explicit evaluation rules (often in multiple-choice format). The classifier evaluates the response against the rubric categories—such as a refusal in the desired style, a refusal in an undesired style (e.g., rambling or evasive), disallowed content, or a safe non-refusal—allowing the system to assign tailored rewards or penalties based on the classification outcome.
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Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Ch.3 Model Alignment and Safety - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Model-Assisted Safety and Rule-Based Reward Models - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
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How are rule-based reward models (RBRMs) categorized in terms of classifier type?
Rule-based reward models (RBRMs) rely solely on scalar human preference models to enforce safety policies.
During which training phase do rule-based reward models (RBRMs) supply additional reward signals to the policy model?
Explain the two key safety objectives that rule-based reward models (RBRMs) are designed to balance regarding model refusals.
Inputs and Rubric Classification Mechanism of RBRMs
Learn After
Match each input processed by a rule-based reward model (RBRM) to its accurate description.
Order the steps involved in evaluating a policy response using a rule-based reward model (RBRM).
Identify the rubric category that the RBRM classifier should assign to this output, and explain what downstream action this classification enables the system to take.