This paper explores Large Action Models (LAMs) that extend beyond text generation to perform real-world actions in physical and digital environments through systematic training and deployment frameworks.
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https://arxiv.org/abs/2412.10047
🤖 Original Problem:
LLMs excel at generating text but struggle to perform real-world actions. They lack the ability to directly interact with environments or execute concrete tasks, limiting their practical applications.
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🛠️ Solution in this Paper:
→ LAMs build upon LLMs but are specifically optimized for action-oriented tasks through a four-phase training pipeline
→ Phase 1 involves task-plan pretraining to develop foundational planning capabilities
→ Phase 2 implements expert demonstrations through imitation learning
→ Phase 3 enables self-boosting exploration where the model tackles previously failed tasks
→ Phase 4 incorporates reinforcement learning with reward models for optimized decision-making
→ The solution integrates the trained LAM into an agent framework with tools, memory systems and feedback loops
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💡 Key Insights:
→ LAMs can be smaller than general-purpose LLMs while achieving better performance in specific domains
→ Dynamic planning and adaptation capabilities are crucial for handling complex multi-step tasks
→ Memory systems and feedback loops significantly improve decision-making accuracy
→ Safety mechanisms and thorough evaluation are essential before real-world deployment
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📊 Results:
→ Achieved 81.2% Task Success Rate, outperforming GPT-4 (67.2%)
→ Reduced task completion time to 30.42 seconds vs GPT-4's 86.42 seconds
→ Demonstrated 5.41 seconds average step latency compared to GPT-4's 12.84 seconds
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