Value Augmented Sampling for Language Model Alignment and Personalization (2405.06639v1)

Abstract: Aligning LLMs to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem. Search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are performant, but impractical for LLM adaptation due to their high inference cost. On the other hand, using Reinforcement Learning (RL) for adaptation is computationally efficient, but performs worse due to the optimization challenges in co-training the value function and the policy. We present a new framework for reward optimization, Value Augmented Sampling (VAS), that can maximize different reward functions using data sampled from only the initial, frozen LLM. VAS solves for the optimal reward-maximizing policy without co-training the policy and the value function, making the optimization stable, outperforming established baselines, such as PPO and DPO, on standard benchmarks, and achieving comparable results to Best-of-128 with lower inference cost. Unlike existing RL methods that require changing the weights of the LLM, VAS does not require access to the weights of the pre-trained LLM. Thus, it can even adapt LLMs (e.g., ChatGPT), which are available only as APIs. In addition, our algorithm unlocks the new capability of composing several rewards and controlling the extent of each one during deployment time, paving the road ahead for the future of aligned, personalized LLMs.

Understanding Value Augmented Sampling for Enhancing LLMs

Introduction to LLM Personalization

LLMs like GPT or BERT have taken giant strides in understanding and generating human-like text. These models are often pre-trained on vast amounts of data and then fine-tuned for specific applications. However, aligning these models more closely with individual user preferences, or teaching them new capabilities without extensive retraining, remains challenging. Common strategies like reinforcement learning (RL) face optimization difficulties, and methods like Best-of-N, although effective, are computationally expensive.

Enter Value Augmented Sampling (VAS)

Value Augmented Sampling (VAS) emerges as a novel strategy to personalize LLMs with enhanced efficiency. It uniquely bypasses the weight adjustments typical of traditional RL methods and the computational heaviness of methods like Best-of-N. VAS operates by augmenting the model's output decisions (token predictions) based on pre-calculated value functions related to the desired output characteristics. This way, the model doesn’t learn from scratch but adapts its behaviors based on these supplemental value cues.

Key Highlights of VAS:

1. Efficiency in Computation: VAS displays remarkable computational efficiency, achieving results comparable to Best-of-128 while being up to six times less resource-intensive.
2. Stability and Performance: It bypasses the common pitfalls of RL methods, offering more stable optimization and robust performance enhancements across different tasks like summarization and dialogues.
3. Adaptation Without Weight Access: Particularly advantageous is VAS’s ability to adapt models without accessing their underlying weights. This means even commercially available models provided as APIs can be personalized and improved using VAS.
4. Real-Time Customization: Users can alter the model's behavior in real-time by adjusting the influence of the value functions during model inference.

Practical Implications and Theoretical Contributions

VAS introduces practical flexibility and theoretical elegance into the field of machine learning for LLMs. Its ability to dynamically compose multiple reward functions during deployment is a significant leap forward, allowing for real-time model personalization based on user feedback or changing requirements. Moreover, this method's efficacy without needing weight adjustments opens new doors for utilizing proprietary models securely and effectively.

The Scalability and Accessibility Edge:

• VAS’s approach means smaller models can be employed to guide larger ones, making it computationally accessible for scenarios with limited resources.
• For businesses using API-accessible models, VAS offers a way to enhance capabilities without breaching proprietary model constraints.

The Future and Beyond

Looking ahead, VAS not only symbolizes a step towards more personalized and efficient use of LLMs but also paves the way for future innovations where models can seamlessly adapt to diverse user needs without comprehensive retraining. The method’s potential in handling multiple alignment metrics simultaneously promises an exciting frontier for developers and researchers aiming to craft more responsive and context-aware AI systems.

Future studies might explore extending the capabilities of VAS, perhaps through incorporating automated learning of value functions based on real-time user interactions, thereby continuously evolving model alignment without manual recalibrations. Additionally, exploring its applicability in other domains of AI, such as vision and speech, could broaden VAS's impact, making it a cornerstone technique in the broader AI adaptation and personalization landscape.

In conclusion, Value Augmented Sampling offers a robust framework for the practical deployment of personalized LLMs, combining theoretical innovation with tangible performance improvements, all while keeping computational efficiency in mind.