Batched Contextual Reinforcement

BCR is a single-stage training paradigm that trains a model to solve N problems at once inside a shared context window, rewarded purely by per-instance accuracy. The implicit token budget this creates cuts token usage by 15.8–62.6% while maintaining or improving accuracy across five mathematical benchmarks, and avoids the optimization collapse that explicit length penalties induce.