VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models

Fuente: arXiv
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Main Authors: Liu, Chonghan, Du, Yimin, An, Qi, He, Xin, Zhai, Cunqi, Tan, Fei, Lin, Weijia, Gong, Xiaochun, Deng, Yongchao, Jia, Shousheng, Zhang, Xiangzheng
Format: Preprint
Published: 2026
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author Liu, Chonghan
Du, Yimin
An, Qi
He, Xin
Zhai, Cunqi
Tan, Fei
Lin, Weijia
Gong, Xiaochun
Deng, Yongchao
Jia, Shousheng
Zhang, Xiangzheng
author_facet Liu, Chonghan
Du, Yimin
An, Qi
He, Xin
Zhai, Cunqi
Tan, Fei
Lin, Weijia
Gong, Xiaochun
Deng, Yongchao
Jia, Shousheng
Zhang, Xiangzheng
contents Large language models frequently exhibit suboptimal performance on low resource languages, primarily due to inefficient subword segmentation and systemic training data imbalances. In this paper, we propose Variable Entropy Policy Optimization (VEPO), which leverages Reinforcement Learning with Verifiable Rewards to incorporate deterministic structural constraints into the policy alignment process. This framework ensures prescribed sequence length, robust format consistency, and rigorous linguistic well formedness, all enforced during training. Central to our approach is a variable entropy mechanism that enables the model to dynamically calibrate the equilibrium between literal fidelity and semantic naturalness by modulating the exploration exploitation manifold. By integrating entropy tempered advantage estimation with asymmetric clipping, VEPO sustains robust exploration while mitigating policy collapse. Empirical evaluations across 90 FLORES-200, COMET-22, chrF directions demonstrate that VEPO yields substantial improvements in both tokenization efficiency and translation quality, bridging the performance gap for underrepresented languages.
format Preprint
id arxiv_https___arxiv_org_abs_2603_19152
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models
Liu, Chonghan
Du, Yimin
An, Qi
He, Xin
Zhai, Cunqi
Tan, Fei
Lin, Weijia
Gong, Xiaochun
Deng, Yongchao
Jia, Shousheng
Zhang, Xiangzheng
Computation and Language
Artificial Intelligence
Large language models frequently exhibit suboptimal performance on low resource languages, primarily due to inefficient subword segmentation and systemic training data imbalances. In this paper, we propose Variable Entropy Policy Optimization (VEPO), which leverages Reinforcement Learning with Verifiable Rewards to incorporate deterministic structural constraints into the policy alignment process. This framework ensures prescribed sequence length, robust format consistency, and rigorous linguistic well formedness, all enforced during training. Central to our approach is a variable entropy mechanism that enables the model to dynamically calibrate the equilibrium between literal fidelity and semantic naturalness by modulating the exploration exploitation manifold. By integrating entropy tempered advantage estimation with asymmetric clipping, VEPO sustains robust exploration while mitigating policy collapse. Empirical evaluations across 90 FLORES-200, COMET-22, chrF directions demonstrate that VEPO yields substantial improvements in both tokenization efficiency and translation quality, bridging the performance gap for underrepresented languages.
title VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2603.19152