RL from Teacher-Model Refinement: Gradual Imitation Learning for Machine Translation
This addresses generalization issues in machine translation for researchers and practitioners, though it appears incremental as it builds on existing preference-learning methods.
The paper tackles the problem of preference-learning methods for machine translation struggling with generalization and reliance on static datasets by proposing RLfR, a framework using continuous feedback from GPT-4o, which improved COMET and M-ETA scores on the FLORES-200 benchmark across multiple languages.
Preference-learning methods for machine translation (MT)--such as Direct Preference Optimization (DPO)--have achieved impressive gains but depend heavily on large, carefully curated triplet datasets and often struggle to generalize beyond their tuning domains. We propose Reinforcement Learning from Teacher-Model Refinement (RLfR), a novel framework that removes reliance on static triplets by leveraging continuous, high-quality feedback from an external teacher model (GPT-4o). RLfR frames each translation step as a micro-tutorial: the actor generates a hypothesis, the teacher refines it, and the actor is rewarded based on how closely it aligns with the teacher's refinement. Guided by two complementary signals--(i) negative edit distance, promoting lexical and structural fidelity, and (ii) COMET score, ensuring semantic adequacy--the actor progressively learns to emulate the teacher, mirroring a human learning process through incremental, iterative improvement. On the FLORES-200 benchmark (English to and from German, Spanish, Chinese, Korean, and Japanese), RLfR consistently outperforms both MT-SFT and preference-based baselines, significantly improving COMET (semantic adequacy) and M-ETA (entity preservation) scores.