Literary translation now rewards a large model's scores: reinforcement learning on source sentences alone lifts a 7B model past QwQ-32B-preview on metaphor-heavy text
Literary translation now gets its reinforcement-learning reward from a large model scoring against written criteria, instead of human preference labels or paired reference translations. Earlier DRT reasoning translation models were supervised on synthesized long thought and translation data, and reinforcement-learning routes such as R1-T1 and MT-R1 used machine translation metrics like COMET and CometKiwi as reward. Built on Qwen2.5-7B, DeepTrans-7B improves over its backbone by 16.3%, 13.8% and 9.0% on GEA5, GEA100 and GRF on MetaphorTrans, and beats sub-30B baselines on most metrics.