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Assigning reward credit to specific tokens, rather than uniformly, dramatically improves multi-reward RL for LLMs.
An information-efficiency ratio (IER) based on a signal-to-noise decomposition and a candidate-set approximation enables token selection based on IER and its combination with existing usefulness scores, while retaining the sampled reverse-KL training objective.