The Detrimental Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning

Keskeiset käsitteet

Fine-tuning language models to the rewards in text-based reinforcement learning leads to semantic degeneration, which decreases the training efficiency and generalization ability of the agents.

Tiivistelmä

The paper investigates the effects of fine-tuning language models (LMs) for text-based reinforcement learning (TBRL) agents. The key findings are:

Semantic information from pre-trained LMs accelerates the training of TBRL agents compared to using hash-based or simple word embedding encoders. Fixed pre-trained LMs outperform fine-tuned LMs in terms of training efficiency and generalization.

Fine-tuning LMs to the rewards in TBRL leads to semantic degeneration, where the LM loses its general semantic understanding and becomes overly specialized to the training games. This causes the RL agent to receive inconsistent and less informative representations, hindering its performance.

Agents using fixed pre-trained LMs exhibit stronger robustness to perturbations like paraphrasing and lexical substitution, as they can maintain the semantic relationships learned during pre-training. In contrast, fine-tuned LMs struggle with such out-of-distribution examples.

The authors conclude that while fine-tuning LMs is a common approach in TBRL, it can be detrimental due to semantic degeneration. Preserving the general semantic understanding from pre-training is crucial for efficient and generalizable TBRL agents.

Tilastot

"Text-based games can be modeled as partially observable Markov decision processes (POMDP) defined by the tuple ⟨S, A, O, T, E, R⟩, where S is the set of states, A the set of actions, O the observation space, T the set of state transition probabilities, E is the conditional observation emission probabilities, and R : S × A →R the reward function."
"The goal of a TBG agent is to reach the end of the game by interacting with the environment through text, while maximizing the final score."

Lainaukset

"We hypothesize that this approach may cause the language model to overfit the training games, leading to the degeneration of the semantic relationships learned during pretraining, and, subsequently, negatively impacting the agent's training efficiency and transfer learning capacity."
"Our results indicate fine-tuning LMs to rewards leads to a decrease in the agent's performance and hinders its ability to play versions of the training games where the observations and actions are slightly reworded, such as through paraphrasing or lexical substitution (synonyms)."

Tärkeimmät oivallukset

On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning

by Mauricio Gru... klo arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10174.pdf

Syvällisempiä Kysymyksiä

How can we fine-tune language models for text-based reinforcement learning while preserving the general semantic understanding from pre-training?

In order to fine-tune language models (LMs) for text-based reinforcement learning (RL) while maintaining the general semantic understanding acquired during pre-training, several strategies can be employed:

Gradual Fine-Tuning: Instead of directly fine-tuning the entire LM on the RL task, a gradual fine-tuning approach can be adopted. This involves freezing certain layers of the LM responsible for capturing general semantic information while only updating the top layers that are task-specific. By doing so, the model retains its semantic understanding while adapting to the RL task.

Regularization Techniques: Incorporating regularization techniques such as weight decay or dropout during fine-tuning can help prevent the model from overfitting to the RL task data. Regularization encourages the model to maintain its general semantic knowledge while learning task-specific information.

Multi-Task Learning: Training the LM on a combination of pre-training objectives and RL objectives simultaneously can help in preserving semantic understanding. By jointly optimizing the LM on multiple tasks, the model can learn to balance between general language understanding and task-specific knowledge.

Knowledge Distillation: Utilizing knowledge distillation, where a larger pre-trained LM acts as a teacher to a smaller RL-specific LM, can transfer the semantic knowledge from the teacher model to the student model. This approach helps in preserving the semantic understanding while fine-tuning for the RL task.

Semantic Regularization Loss: Introducing a semantic regularization loss term during fine-tuning can encourage the model to maintain semantic relationships between words and concepts. This additional loss term penalizes the model for deviating too much from its pre-trained semantic representations.

By implementing these techniques, it is possible to fine-tune language models for text-based RL tasks while safeguarding the general semantic understanding acquired during pre-training.

How do the findings in this paper apply to other domains beyond text-based games, where language models are used to encode input for reinforcement learning?

The findings in this paper have broader implications beyond text-based games and can be applied to various domains where language models are utilized to encode input for reinforcement learning. Some of the key applications include:

Dialogue Systems: In conversational AI systems, where language models are employed to understand and generate human-like responses, preserving semantic understanding during fine-tuning is crucial. By avoiding semantic degeneration, these systems can maintain coherent and contextually relevant conversations.

Autonomous Vehicles: Language models are increasingly being used in autonomous vehicles for natural language interaction with passengers and pedestrians. Ensuring that the models retain their semantic knowledge while adapting to real-world scenarios is essential for safe and effective communication.

Healthcare: In medical applications, language models assist in analyzing patient records, generating reports, and providing recommendations. By preserving semantic understanding, these models can offer accurate and context-aware insights to healthcare professionals.

Financial Services: Language models play a role in analyzing financial data, generating reports, and assisting in decision-making processes. Maintaining semantic coherence is critical in ensuring that the models provide accurate and reliable information for financial tasks.

By applying the insights from this paper to these domains, practitioners can enhance the performance and robustness of language models in various real-world applications beyond text-based games.

What other techniques, beyond fine-tuning, can be used to effectively incorporate semantic information into text-based RL agents?

In addition to fine-tuning, several other techniques can be employed to effectively incorporate semantic information into text-based RL agents:

Semantic Embeddings: Utilizing pre-trained semantic embeddings such as Word2Vec, GloVe, or FastText can provide rich semantic representations of words and phrases. These embeddings capture semantic relationships between tokens and can enhance the semantic understanding of the RL agent.

Ontology Integration: Integrating domain-specific ontologies or knowledge graphs into the RL agent's architecture can provide structured semantic information. By leveraging ontological relationships, the agent can make more informed decisions based on semantic context.

Semantic Parsing: Implementing semantic parsing techniques to convert natural language input into formal representations of meaning can help the RL agent understand the underlying semantics of the text. This parsed semantic information can guide the agent's actions more effectively.

Semantic Role Labeling: Incorporating semantic role labeling models can assist in identifying the roles of words and phrases in a sentence, enabling the RL agent to comprehend the semantic structure of the input text. This information can aid in action selection and decision-making.

Contextual Embeddings: Leveraging contextual embeddings from models like BERT, RoBERTa, or GPT can capture the contextual semantics of the input text. These embeddings provide a deeper understanding of the text's meaning in different contexts, enhancing the agent's semantic comprehension.

By integrating these techniques into the design and training of text-based RL agents, practitioners can enhance the semantic understanding and decision-making capabilities of the agents in various applications.