Reasoning Model
A reasoning model is an AI language model specifically optimized to think through problems step-by-step using extended internal deliberation before producing a final answer, achieving higher accuracy on complex reasoning tasks.
理解する Reasoning Model
Traditional LLMs generate responses token by token without an explicit deliberation phase. Reasoning models introduce a thinking phase where the model works through a problem internally before producing its final answer. This extended internal reasoning allows the model to explore multiple approaches, identify errors in its own reasoning, and arrive at more accurate conclusions for complex tasks. Reasoning models trade inference speed for accuracy, making them suited to complex planning, mathematical reasoning, and multi-step problem solving rather than simple conversation.
GAIAの活用方法 Reasoning Model
GAIA supports reasoning models as the LLM backend for complex planning tasks. When orchestrating multi-step workflows or making complex scheduling decisions with many constraints, a reasoning model's extended deliberation produces better outcomes than standard generation. GAIA can route different tasks to different model types based on their complexity and latency requirements.
関連概念
Chain-of-Thought Reasoning
Chain-of-thought (CoT) reasoning is a prompting technique that instructs an AI model to articulate its intermediate reasoning steps before producing a final answer, significantly improving accuracy on complex multi-step problems.
大規模言語モデル（LLM）
大規模言語モデル（LLM）は、膨大なテキストデータでトレーニングされた人工知能モデルであり、人間のような流暢さで言語を理解、生成、推論できます。
Foundation Model
A foundation model is a large AI model trained on broad data at scale that can be adapted to a wide range of downstream tasks through fine-tuning, prompting, or integration into application architectures.
AIオーケストレーション
AIオーケストレーションとは、単独では処理できない複雑なマルチステップタスクを完了するために、複数のAIエージェント、モデル、およびツールを連携させることです。
Reinforcement Learning
Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by receiving reward signals for actions that achieve desired outcomes and penalty signals for undesired ones.
