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4 changes: 2 additions & 2 deletions chapters/01-introduction.md
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Expand Up @@ -14,7 +14,7 @@ This book details key decisions and basic implementation examples for each step
RLHF has been applied to many domains successfully, with complexity increasing as the techniques have matured.
Early breakthrough experiments with RLHF were applied to deep reinforcement learning [@christiano2017deep], summarization [@stiennon2020learning], follow instructions [@ouyang2022training], parse web information for question answering [@nakano2021webgpt], and "alignment" [@bai2022training].

In modern language model training, RLHF is one component on post-training.
In modern language model training, RLHF is one component of post-training.
Post-training is a more complete set of techniques and best-practices to make language models more useful for downstream tasks [@lambert2024t].
Post-training can be summarized as using three optimization methods:

Expand Down Expand Up @@ -166,4 +166,4 @@ $$ y = mx + b $$ {#eq:equation}
| 1 | BBB |
| ... | ... |

Table: This is an example table. {#tbl:table} -->
Table: This is an example table. {#tbl:table} -->