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LLM Agents Optimize Costs via Skill Rewriting and Translation Policies

By PulseAugur Editorial · [5 sources] ·

Researchers are exploring cost-aware strategies for large language model agents to improve efficiency and performance. One paper introduces a framework for skill rewriting that optimizes for cost by preserving essential operational anchors, leading to reduced agent costs. Another study focuses on cost-aware translation tool use, developing a reinforcement learning policy that intelligently decides when to translate inputs to leverage LLM capabilities without unnecessary expense, particularly benefiting low-resource languages. A third paper presents a reinforcement learning framework for source rewriting in machine translation that directly optimizes for downstream translation quality, outperforming prompt-based methods. AI

IMPACT These research papers suggest new methods for improving the efficiency and effectiveness of LLM agents and translation systems, potentially leading to more capable and cost-efficient AI applications.

RANK_REASON The cluster contains multiple academic papers detailing novel research methodologies and findings in the field of AI, specifically concerning LLM agents and machine translation.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

COVERAGE [5]

  1. arXiv cs.AI TIER_1 English(EN) · Boxuan Lyu, Haiyue Song, Zhi Qu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura ·

    Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation

    arXiv:2606.08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for differen…

  2. arXiv cs.CL TIER_1 English(EN) · Zhiwei Xiong ·

    What Should a Skill Remember? Quality-Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents

    Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. Skill rewriting is often treated as prompt compression, but shorter skills can make agents more expen…

  3. arXiv cs.CL TIER_1 English(EN) · Pratik Jayarao, Chaitanya Dwivedi, Himanshu Gupta, Neeraj Varshney, Adithya M Devraj, Meet Vadera, Priyanka Nigam, Bing Yin ·

    Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning

    arXiv:2606.06835v1 Announce Type: new Abstract: The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most languages, do not exist. Translation offers an alternative: converting an input i…

  4. arXiv cs.CL TIER_1 English(EN) · Manabu Okumura ·

    Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation

    Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinfo…

  5. arXiv cs.CL TIER_1 English(EN) · Bing Yin ·

    Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning

    The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most languages, do not exist. Translation offers an alternative: converting an input into the model's dominant language unlocks its fu…

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