aiqbench analysis
This paper introduces TIAO, a reinforcement learning strategy for text summarization that differentiates token importance to improve model consistency and coherence. By reweighting trajectory advantages based on core token dependencies, the method enables smaller 7B foundation models to match the performance of leading proprietary systems like GPT-4. The approach represents a significant advancement in optimizing Large Language Models through more granular reward signal application.
TIAO: Token Importance-Aware Policy Optimization for Text Summarization
arXiv:2609.16748v1 Announce Type: new Abstract: Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved through reinforcement learning (RL). However, most existing methods apply reward signals directly to undifferentiated token sequences, overlooking the varying importance of individual tokens to word and sentence level quality in summarization. In this paper, we propose Token Importance-Aware Policy Optimization (TIAO), a novel reinforcement learning strategy that explicitly leverages token-importance awareness. Specifically, TIAO identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies. Experiments on the real world dataset show that our TIAO achieves highly competitive results, and that a 7B foundation model enhanced by TIAO performs comparably to GPT-4 and GPT-5-nano. Code is available at https://github.com/TechCloud-x/TIAO
Abstract:Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved through reinforcement learning (RL). However, most existing methods apply reward signals directly to undifferentiated token sequences, overlooking the varying importance of individual tokens to word and sentence level quality in summarization. In this paper, we propose Token Importance-Aware Policy Optimization (TIAO), a novel reinforcement learning strategy that explicitly leverages token-importance awareness. Specifically, TIAO identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies. Experiments on the real world dataset show that our TIAO achieves highly competitive results, and that a 7B foundation model enhanced by TIAO performs comparably to GPT-4 and GPT-5-nano. Code is available at this https URL
Computation and Language (cs.CL)
arXiv:2609.16748 [cs.CL]
(or arXiv:2609.16748v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.16748 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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