Sensi: Learn One Thing at a Time -- Curriculum-Based Test-Time Learning for LLM Game Agents
#Sensi #curriculum-based learning #test-time learning #LLM agents #game AI
📌 Key Takeaways
- Sensi introduces a curriculum-based test-time learning method for LLM game agents.
- The approach focuses on learning one skill at a time to improve agent performance.
- It aims to enhance adaptability and efficiency in game environments through structured training.
- The method leverages test-time learning to refine agent capabilities during evaluation.
📖 Full Retelling
arXiv:2603.17683v1 Announce Type: new
Abstract: Large language model (LLM) agents deployed in unknown environments must learn task structure at test time, but current approaches require thousands of interactions to form useful hypotheses. We present Sensi, an LLM agent architecture for the ARC-AGI-3 game-playing challenge that introduces structured test-time learning through three mechanisms: (1) a two-player architecture separating perception from action, (2) a curriculum-based learning system
🏷️ Themes
AI Training, Game Agents
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Original Source
arXiv:2603.17683v1 Announce Type: new
Abstract: Large language model (LLM) agents deployed in unknown environments must learn task structure at test time, but current approaches require thousands of interactions to form useful hypotheses. We present Sensi, an LLM agent architecture for the ARC-AGI-3 game-playing challenge that introduces structured test-time learning through three mechanisms: (1) a two-player architecture separating perception from action, (2) a curriculum-based learning system
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