R3: Robust rubric-agnostic reward models
A reward modeling framework that is rubric-agnostic, generalizable, and provides interpretable, reasoned score assignments.
Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models are typically optimized for narrow objectives, limiting their generalizability to broader downstream tasks. Moreover, their scalar outputs are difficult to interpret without contextual reasoning. To address these limitations, we introduce R3, a novel reward modeling framework that is rubric-agnostic, generalizable across evaluation dimensions, and provides interpretable, reasoned score assignments. R3 enables more transparent and flexible evaluation of language models, supporting robust alignment with diverse human values and use cases.
Latest publications
RECAP: Regression evaluation for continual adaptation of prompts
A benchmark that measures continual-learning phenomena at the constraint level for prompt-level adaptation methods.
EMNLPMEMGUARD: Preventing memory contamination in long-term memory-augmented large language models
A type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval.
EMNLPA history-aware visually grounded critic for computer use agents
A test time intervention framework for long-horizon GUI agents.
EMNLP