February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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3
エージェント
軌跡
（短期記憶）
Actor
Self-Reflection
言語的
フィードバック
プロンプト
プロンプト
推論結果
環境
行動観測結果
経験
（長期記憶）
Evaluator
図3.9.1
各構成要素の働きは次のとおりです。
Actor：与えられたタスクを試行します。現在の試行における軌跡（推論や行動と観測の
シーケンス）を短期記憶として保持します。また、過去の試行で得られたSelf-Reflection
からのフィードバックを長期記憶として参照します。
Evaluator：短期記憶に格納されたActor の試行結果である軌跡を評価します。
Self-Reflection：現在の試行における軌跡とEvaluator の評価結果を入力として受け取り、
言語的なフィードバックを生成します。このフィードバックは、長期記憶に追加されま
す。
なお、短期記憶はタスクの試行ごとに完結し、試行が終了すると破棄されます。一方、長期
記憶は複数の試行をまたいで蓄積されていきます。この設計により、各エージェントは自身の
役割に特化した情報を扱うことができます。また、長期記憶を通じてActor が過去の経験から
学習することで、類似タスクを繰り返すうちに性能が向上していきます。
3.9.2
Reflexionの処理手順
あるタスクが与えられたときのReflexion の処理手順は次のとおりです。
1. Actorが初期の軌跡を生成
2. Evaluatorが軌跡を評価
151
3.9
Reflexion
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.