ALAS: Autonomous Learning Agent for Self-Updating Language Models

• URL: http://arxiv.org/abs/2508.15805v1
• Date: Thu, 14 Aug 2025 06:55:51 GMT
• Title: ALAS: Autonomous Learning Agent for Self-Updating Language Models
• Authors: Dhruv Atreja,
• Abstract summary: Large language models (LLMs) often have a fixed knowledge cutoff, limiting their accuracy on emerging information.<n>We present ALAS, a modular pipeline that continuously updates an LLM's knowledge with minimal human intervention.<n>ALAS autonomously generates a learning curriculum for a target domain, retrieves up-to-date information from the web (with citations), distills this into question-answer training data, and fine-tunes the model through supervised fine-tuning.<n>We show that ALAS achieves 90% accuracy on knowledge-updated queries with minimal engineering overhead.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) often have a fixed knowledge cutoff, limiting their accuracy on emerging information. We present ALAS (Autonomous Learning Agent System), a modular pipeline that continuously updates an LLM's knowledge with minimal human intervention. ALAS autonomously generates a learning curriculum for a target domain, retrieves up-to-date information from the web (with citations), distills this into question-answer training data, and fine-tunes the model through supervised fine-tuning (SFT) and direct preference optimization (DPO). It iteratively evaluates performance and revises the curriculum, enabling long-term continual learning. We demonstrate ALAS's ability to self-improve a model on rapidly evolving domains (e.g., new Python releases, latest security CVEs, academic trends), significantly boosting post-cutoff question answering accuracy (from 15% to 90% on average) without manual dataset curation. The system emphasizes modularity and reproducibility: each component (planning, retrieval, distillation, memory, fine-tuning) is interchangeable and built on standard APIs. We discuss comparative baselines (e.g., retrieval-augmented generation vs. fine-tuning) and show that ALAS achieves 90% accuracy on knowledge-updated queries with minimal engineering overhead. Finally, we outline limitations (cost, dependency on source quality) and future directions for autonomous lifelong learning in LLMs.

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