Summary of Agent Skill Acquisition For Large Language Models Via Cycleqd, by So Kuroki et al.
Agent Skill Acquisition for Large Language Models via CycleQD
by So Kuroki, Taishi Nakamura, Takuya Akiba, Yujin Tang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach, CycleQD, leverages the Quality Diversity framework through a cyclic adaptation of the algorithm, model merging based crossover, and SVD-based mutation. This allows for concentrated effort on one task at a time, eliminating the need for data ratio tuning and simplifying the design of the objective function. The method enables LLAMA3-8B-INSTRUCT based models to surpass traditional fine-tuning methods in coding, operating systems, and database tasks, while achieving performance on par with GPT-3.5-TURBO across these domains. CycleQD retains robust language capabilities, as evidenced by its performance on widely adopted language benchmark tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CycleQD is a new way to train large language models. It helps them learn specific skills better than usual methods do. This approach focuses on one task at a time and simplifies the way it measures success. As a result, CycleQD lets language models perform well in areas like coding and operating systems. It even matches the performance of GPT-3.5-TURBO in these domains. Most importantly, CycleQD keeps language capabilities strong. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Objective function
