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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5. 学習：得られた経験をもとに方策を更新する
強化学習では1 から 5を繰り返すことでより良い方策を学習します。
2.4.2
強化学習のLLM への適用
RLHFでは、強化学習の枠組みに LLM を次のように適用します。
エージェント：LLM
環境：テキスト生成タスク
状態：現在のテキスト文脈
行動：次のトークンの選択
報酬：報酬モデルの出力
方策： LLM のパラメータ
また、強化学習の基本的なステップをRLHF に当てはめると次のようになります。
1. 観察：LLM が現在のテキスト文脈（プロンプトと生成済みのテキスト）を観察
2. 行動選択：LLM が次のトークンを生成
3. 行動実行：LLM が次のトークンを生成し、テキストに追加
4. 結果観察：生成されたテキスト全体に対して、報酬モデルがスコアを計算
5. 学習：報酬モデルのスコアを最大化するように LLM のパラメータを更新
RLHFでは1 から5 を繰り返すことで、人間の好みにより適合する出力を得られるように
LLMのパラメータを調整します。
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