Reward no longer goes only to the nearest action: letting a language model explain outcomes traces delayed credit to its source
A credit-assignment framework that redistributes delayed reward by explanation rather than temporal proximity traces sparse terminal reward back to the actions that actually mattered hundreds of steps earlier, across Crafter, WordCraft, KuaiRand, and an industry-scale generative recommender.