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Data Science
Analytics Vidhya·Jul 3·5 min read
Large Action Models (LAMs) vs Agentic LLMs: What’s the Real Difference?
TL;DR
A significant gap exists between Large Action Models (LAMs) and agentic LLMs, which are often misunderstood. LAMs, like ChatGPT, focus on predicting the next word, while agentic LLMs incorporate a reasoning loop called ReAct, allowing them to choose tools and make decisions based on goals.
✦ Why It Matters
Engineers can choose the right model type for their specific AI application needs, enhancing task performance.
Key Takeaways
- Use LAMs for reliable execution of bounded tasks.
- Leverage agentic LLMs for open-ended reasoning tasks.
- Consider model size and training data for task suitability.
- Combine both models for optimal performance in production systems.
How It Works
LAMs are trained specifically to generate correct actions from the start, using data on action trajectories. This contrasts with agentic LLMs, which rely on a reasoning loop to derive actions from text predictions.
The architecture of LAMs follows a perceive, plan, act, learn cycle, embedding action capability directly into the model.