Reinforcement Learning for NLP
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
This paper introduces MT-R1-Zero, the first open-source adaptation of the R1-Zero RL framework for Machine Translation without supervised fine-tuning. It proposes a rule-metric mixed reward mechanism to guide LLMs towards improved translation quality via emergent reasoning. Experiments on WMT 24 EN-ZH show competitive performance, surpassing TowerInstruct-7B-v0.2 and even matching proprietary models like GPT-40 in some metrics. The framework also demonstrates strong generalization capabilities across OOD and low-resource settings. Key findings highlight the critical role of reward design, LLM adaptability, and emergent reasoning patterns.
Key Impact Metrics
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WMT24 EN-ZH Avg. Score
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Improvement over SFT
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OOD Generalization
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MT-R1-Zero adapts the R1-Zero RL framework for MT by introducing a rule-metric mixed reward mechanism. This hybrid reward consists of a Format Reward (checking output structure) and a Metric Reward (evaluating translation quality). The Metric Reward can be Lexical (BLEU), Semantic (COMETKiwi), or a Mix of both.
Training uses the Group Relative Policy Optimization (GRPO) algorithm to ensure stable and efficient RL training. The framework incentivizes emergent reasoning by guiding the LLM to provide thinking processes within <think></think> tags and the final translation within <translate></translate> tags.
Reward metric selection critically shapes optimization targets and translation style. Lexical rewards optimize for BLEU, semantic for COMETKiwi, and mixed rewards provide a balance. Response length initially declines, then increases, reflecting evolving reasoning strategies from naive decomposition to richer semantic analysis. Diverse reasoning patterns emerge autonomously, and internal reasoning language can dynamically transit to target languages even for OOD settings. LLM architectures exhibit distinct adaptability, with Qwen models showing high compatibility compared to LLaMA and Tower, which tend towards 'format hacking'.
MT-R1-Zero demonstrates strong out-of-distribution (OOD) generalization across unseen language pairs (e.g., EN-JA, DE-EN, DE-ZH) with zero-shot settings. The quality improvements effectively transfer. It also shows multilingual and low-resource support, maintaining effectiveness across languages like Icelandic and Norwegian. Performance gains are primarily driven by the RL process itself, rather than explicit reasoning steps or verbosity.
62.25
Average Score (BLEU, COMETKiwi, XCOMET) on WMT 24 EN-ZH for MT-R1-Zero-7B-Mix
MT-R1-Zero Training Process
Input Source Text
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LLM Generates Thought & Translation
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Format Reward Check
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Metric Reward Evaluation
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GRPO Optimization
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Improved LLM Policy
| Model | Key Advantages | Limitations |
|---|---|---|
| MT-R1-Zero-7B-Mix |
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| GPT-4o / Claude-3.5-Sonnet |
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| TowerInstruct-7B-v0.2 |
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Dynamic Language-of-Thought in OOD Settings
MT-R1-Zero models exhibit a striking 'language-of-thought' phenomenon during OOD testing. While base models often default to English for internal reasoning, MT-R1-Zero progressively transitions to utilize the target language of the translation task within its <think></think> block. This dynamic adaptation, conditioned by the task, emerges even without direct supervision on reasoning language, showcasing the framework's ability to foster deep, context-aware reasoning beyond simple output generation.
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