mlsanityData to 7 Oct 2026

Supplement Generation Training for Enhancing Agentic Task Performance

arXiv 2604.20727 ↗First posted 22 Apr 2026cs.LG cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are continuously released. Instead of post-training massive models for every new task or domain, we propose Supplement Generation Training (SGT), a more efficient and sustainable strategy. SGT trains a smaller LLM to generate useful supplemental text that, when appended to the original input, helps the larger LLM solve the task more effectively. These lightweight models can dynamically adapt supplements to task requirements, improving performance without modifying the underlying large models. This approach decouples task-specific optimization from large foundation models and enables more flexible, cost-effective deployment of LLM-powered agents in real-world applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 18 of its 30 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

18 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024StreamBench: Towards Benchmarking Continuous Improvement of Language Agents13 Jun

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  5. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
  6. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  7. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  8. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  9. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 443
  10. 2022DS-1000: A Natural and Reliable Benchmark for Data Science Code Generationcited by 442
  11. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  12. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 213
5 more
  1. 2023Black-Box Prompt Optimization: Aligning Large Language Models without Model Trainingcited by 122
  2. 2024In-Context Principle Learning from Mistakescited by 42
  3. 2023Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RLcited by 37
  4. 2024Customizing Language Model Responses with Contrastive In-Context Learningcited by 19
  5. 2024Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyondcited by 18

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