Reinforcement Learning for Training Large Language Models
The rapid advancement and widespread adoption of Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence. ChatGPT, for instance, achieved an unprecedented milestone by acquiring 100 million users shortly after its release, marking the fastest adoption of any internet service [1, 9, 28]. However, alongside their remarkable capabilities, LLMs present significant challenges, including the potential for generating harmful content, exhibiting biases, and vulnerability to adversarial attacks [1, 36]. Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular and effective method for addressing these challenges, aligning LLMs with human values, and ensuring their responsible use [1, 10]. This report explores the use of reinforcement learning in training LLMs, encompassing its origins, current advancements, and future prospects.
Background: The Rise of Large Language Models
Language Models (LMs) operate by calculating the probability of a word following a given input sentence, a process achieved through self-supervised learning on vast amounts of unannotated text [1, 11, 29]. During training, the LM is fed a large corpus of text and tasked with predicting the next word in a sentence, creating an internal representation of language [2, 11, 29]. This foundational training is often followed by fine-tuning, where a pre-trained model undergoes further training on a smaller, task-specific labeled dataset using supervised learning [2, 12, 30]. Transfer learning allows a model to leverage knowledge gained from one task and apply it to another, enhancing efficiency and performance [2, 12, 30].
The architecture of modern LLMs is predominantly based on the Transformer model, introduced in 2017, which revolutionized AI with its ability to process large chunks of data in parallel [3, 13, 31]. Transformers leverage attention mechanisms and word embeddings for natural language contextual understanding [3, 13, 31]. The encoder encodes text into a numerical representation, and the decoder decodes it back into text [3, 32]. BERT, utilizing only the encoder, excels at prediction and classification tasks, while GPT, a decoder-only model, is suited for generating novel text [3, 14, 33].
To ensure LLMs are beneficial and safe, they should ideally be helpful, truthful, and harmless [4, 20, 35]. An LLM is considered "aligned" if it adheres to these guidelines [4, 20, 35]. However, without proper alignment, LLMs can be exploited for malicious purposes, such as creating sophisticated malware or distorting public discourse [21, 34]. They may also inadvertently replicate personally identifiable information or cause psychological harm [21, 34]. Thus, effective methods for controlling and steering LLMs are in high demand [10, 28].
Current Advancements in RLHF for LLMs
The development of LLMs has seen a dramatic increase in size, with some models surpassing 500 billion parameters [1, 15, 33]. The size of LLMs has doubled every 3.5 months on average [1, 15, 33]. Training such models can cost $10-20 million for pre-training alone [1, 16, 33]. However, recent research indicates that many LLMs are significantly undertrained, emphasizing the importance of training with more extensive datasets [1, 17, 33]. Scaling LLMs leads to emergent abilities like translation and code writing [1, 18, 33]. Instruction tuning improves an LLM's ability to follow prompts [1, 19, 33].
RLHF refines a baseline model by prioritizing sequences favored by humans, introducing a 'human preference bias' [6, 22, 35]. It leverages human feedback to generate a human preferences dataset, which is then used to learn a reward function [6, 22, 35]. Human feedback can include preference orderings, demonstrations, corrections, and natural language input [6, 23, 35]. Reinforcement Learning (RL) enables intelligent agents (like an LLM) to learn an optimal policy to maximize a reward [6, 23, 35].
OpenAI's RLHF Process for ChatGPT
OpenAI's RLHF process for ChatGPT involves three steps: supervised fine-tuning (SFT), preference orderings to train a reward model, and reinforcement learning using Proximal Policy Optimization (PPO) [1, 7, 24, 25, 35].
Alternative Preference Optimization Techniques
While RLHF has proven effective, alternative methods for aligning LLMs without reinforcement learning are gaining traction. Direct Preference Optimization (DPO) recasts the alignment formulation as a simple loss function that can be optimized directly on a dataset of preferences [37, 38]. Identity Preference Optimisation (IPO) adds a regularization term to the DPO loss to avoid overfitting [37, 39]. Kahneman-Tversky Optimisation (KTO) can be applied to any dataset where responses are rated positively or negatively, unlike DPO and IPO which require pairs preference data [37, 40].
A study comparing DPO, IPO, and KTO on the OpenHermes-2.5-Mistral-7B and Zephyr-7b-beta-sft models found that DPO and IPO can achieve comparable results, outperforming KTO in a paired preference setting [37, 41, 42, 43, 44]. For the Zephyr model, the best performance was achieved with a beta value of 0.01 across all three algorithms. With the OpenHermes model, the best choice of beta for DPO, KTO and IPO being 0.6, 0.3 and 0.01 respectively [37].
Limitations and Ethical Considerations
RLHF introduces biases into the distribution of the base model, narrowing the potential range of generated content [1, 8, 26, 35]. While RLHF improves the consistency of the model's answers, it does so at the cost of diversity in its generation abilities [1, 8, 26, 35]. This trade-off could be a benefit or limitation, depending on the use case [1, 8, 26, 35].
LLMs can also suffer from social bias, robustness problems, and poisoning issues, leading to the generation of harmful content [36, 45, 48]. Social biases, like racial and gender discrimination, persist even with scaling up LLMs, reflecting biases in the training data [36, 45, 46]. Data may contain unfair or biased characteristics such as a bias towards associating phrases that reference individuals with disabilities with a greater frequency of negative sentiment words or disproportionately prevalent texts pertaining to mental illness covering gun violence, homelessness, and drug addiction [36, 46]. LLMs are vulnerable to adversarial instances, with performance dropping under attacks [36, 45, 48]. Poisoning attacks involve introducing tainted data to trigger specific, often toxic, outputs [36, 45, 48]. Poisoned models may be elicited to generate toxic contents like abusive language, hate speech, violent speech [36, 48]. LLMs' performance can be unstable when changing the choice of prompt format, training examples, and the order of examples when conducting in-context learning [36, 47, 48].
Future Prospects
One approach to alleviating bias is through alignment techniques like RLHF, training LLMs to align with human values and thus mitigate some biases [36, 47]. Future research should focus on developing more robust and unbiased RLHF techniques, as well as exploring alternative alignment methods [36, 47]. Addressing the ethical considerations and limitations of RLHF is crucial for ensuring the responsible development and deployment of LLMs.
Conclusion
Reinforcement learning plays a crucial role in training Large Language Models, enabling them to align with human values and generate more helpful, truthful, and harmless content. While RLHF has achieved remarkable success, it is essential to acknowledge its limitations and ethical considerations. By addressing these challenges and continuing to explore new techniques, we can harness the full potential of LLMs while mitigating their risks. The future of LLMs depends on our ability to develop and implement responsible AI practices, ensuring that these powerful tools benefit society as a whole.
References
[1-35] The Full Story of Large Language Models and RLHF (https://www.assemblyai.com/blog/the-full-story-of-large-language-models-and-rlhf/)
[36] Safety and Ethical Concerns of Large Language Models (https://aclanthology.org/2023.ccl-4.2.pdf)
[37-44] Preference Tuning LLMs with Direct Preference Optimization Methods (https://huggingface.co/blog/pref-tuning)
[45-48] Safety and Ethical Concerns of Large Language Models (https://aclanthology.org/2023.ccl-4.2.pdf)
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