You Only Judge Once: Multi-response Reward Modeling in a Single Forward Pass

YOJO (You Only Judge Once) addresses a critical bottleneck in the RLHF pipeline: the inference cost of Reward Models (RMs). Traditionally, RMs use the Bradley-Terry model to score pairs of responses independently, requiring O(N) forward passes for N candidates. YOJO reduces this to O(1) relative to the number of candidates by batching them into the context window. While technically sound and addressing a high-value problem (efficiency), the project currently lacks a moat. With 0 stars and being only 4 days old, it is a research artifact rather than a product. Frontier labs like OpenAI and Anthropic are heavily incentivized to implement similar efficiencies in their proprietary training stacks. The method—cross-entropy over concatenated responses—is an incremental improvement over existing list-wise ranking approaches (like those seen in recent Google/DeepMind research). This will likely be absorbed into standard RLHF libraries like TRL (Hugging Face) or Alignment-Handbook within months, making the standalone project obsolete quickly.