Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical prediction. However, its progress is hindered by the misalignment between discrete token-level objectives (e.g., cross-entropy) and continuous numerical values. Existing approaches relying on token-level constraints often fail to capture the global magnitude of the target value, limiting their precision and generalization. In this paper, we propose to unlock the potential of decoding-based regression via Reinforcement Learning (RL). We formulate the generation process as a Markov Decision Process, utilizing sequence-level rewards to enforce global numerical coherence. Extensive experiments on tabular regression and code metric regression demonstrate that our method (specifically with ReMax and GRPO) consistently outperforms both state-of-the-art token-level baselines and traditional regression heads, showing the superiority of introducing sequence-level signals. Our analysis further reveals that RL significantly enhances sampling efficiency and predictive precision, establishing decoding-based regression as a robust and accurate paradigm for general-purpose numerical prediction.
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Ming Chen
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Sheng Tang
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Rong-Xi Tan
claimed_verified
2025-12-09T09:26:56.523Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/l09xhBMmqzX7SR7Sf_v0W.png
Rong-Xi Tan
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user
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6937ab1e19d912300c34a33d
Ziniu Li
6937ab1e19d912300c34a33e
Jiacheng Chen
6937ab1e19d912300c34a33f
Ke Xue
6937ab1e19d912300c34a340
Chao Qian
Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical prediction. However, its progress is hindered by the misalignment between discrete token-level objectives (e.g., cross-entropy) and continuous numerical values. Existing approaches relying on token-level constraints often fail to capture the global magnitude of the target value, limiting their precision and generalization. In this paper, we propose to unlock the potential of decoding-based regression via Reinforcement Learning (RL). We formulate the generation process as a Markov Decision Process, utilizing sequence-level rewards to enforce global numerical coherence. Extensive experiments on tabular regression and code metric regression demonstrate that our method (specifically with ReMax and GRPO) consistently outperforms both state-of-the-art token-level baselines and traditional regression heads, showing the superiority of introducing sequence-level signals. Our analysis further reveals that RL significantly enhances sampling efficiency and predictive precision, establishing decoding-based regression as a robust and accurate paradigm for general-purpose numerical prediction.
Reinforcement Learning enhances decoding-based regression by introducing sequence-level rewards, improving precision and generalization over token-level methods.
decoding-based regression large language models regression sequence generation discrete token-level objectives continuous numerical values token-level constraints Reinforcement Learning Markov Decision Process sequence-level rewards global numerical coherence tabular regression code metric regression ReMax GRPO traditional regression heads sampling efficiency predictive precision