Ignore the KL Penalty! Boosting Exploration on Critical Tokens to
  Enhance RL Fine-Tuning
The ability to achieve long-term goals is a key challenge in the current
development of large language models (LLMs). To address this, pre-trained LLMs
can be fine-tuned with reinforcement learning (RL) to explore solutions that
optimize a given goal. However, exploration with LLMs is difficult, as a
balance has to be struck between discovering new solutions and staying close
enough to the pre-trained model, so as not to degrade basic capabilities. This
is typically controlled with a Kullback-Leibler (KL) penalty. In this paper, we
investigate the exploration dynamics of a small language model on a simple
arithmetic task. We show how varying degrees of pre-training influence
exploration and demonstrate the importance of "critical tokens" which have a
dramatic impact on the final outcome. Consequently, we introduce a simple
modification to the KL penalty that favors exploration on critical tokens,
increasing the efficiency of the RL fine-tuning stage.
67accc647e1fcf03e14b1033
Jean Vassoyan
claimed_verified
2025-02-13T08:24:24.993Z
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/avatars/94cf09cf817327be50ecba75f7f60fa1.svg
Jean Vassoyan
user
supertardigrade
67accc647e1fcf03e14b1034
Nathanaël Beau
extracted_pending
2025-02-12T16:29:25.829Z
63da60458658cbc1cc489bd7
/avatars/620ce7ea229de7abe4dc9ea93021f0e4.svg
Nathanaël Beau
user
Nbeau
67accc647e1fcf03e14b1035
Roman Plaud
claimed_verified
2025-02-13T08:24:27.330Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/mDB2nCMAgcX8bQ1u1p7P4.png
Roman Plaud
user
lecraquito
Ignore the KL Penalty! Boosting Exploration on Critical Tokens to
  Enhance RL Fine-Tuning
The ability to achieve long-term goals is a key challenge in the current
development of large language models (LLMs). To address this, pre-trained LLMs
can be fine-tuned with reinforcement learning (RL) to explore solutions that
optimize a given goal. However, exploration with LLMs is difficult, as a
balance has to be struck between discovering new solutions and staying close
enough to the pre-trained model, so as not to degrade basic capabilities. This
is typically controlled with a Kullback-Leibler (KL) penalty. In this paper, we
investigate the exploration dynamics of a small language model on a simple
arithmetic task. We show how varying degrees of pre-training influence
exploration and demonstrate the importance of "critical tokens" which have a
dramatic impact on the final outcome. Consequently, we introduce a simple
modification to the KL penalty that favors exploration on critical tokens,
increasing the efficiency of the RL fine-tuning stage.
A modification to the KL penalty in RL fine-tuning of language models enhances exploration, particularly on critical tokens, improving the efficiency of reaching long-term goals.
reinforcement learning pre-trained LLMs KL penalty exploration critical tokens