MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection
MAVIS achieves multi-objective alignment via inference-time value-guided selection, enhancing Pareto front performance.
Key Findings
Methodology
MAVIS is a lightweight inference-time alignment framework that achieves multi-objective alignment by training a set of small value models, each corresponding to a specific objective. At inference, these models are combined using user-specified weights to produce a tilting function that adjusts the base model's output distribution. The value models are trained using a simple iterative algorithm that ensures monotonic improvement of the KL-regularized policy.
Key Results
- MAVIS outperforms MOD and RSoup in Pareto front expansion on the HH-RLHF dataset, demonstrating superior objective balancing capabilities.
- On the Summarize from Feedback dataset, MAVIS excels in summary quality and factual consistency, especially with the Llama 13B model.
- Experiments show that MAVIS can quickly adapt to new objective weight configurations without retraining the model.
Significance
MAVIS provides a flexible and efficient solution to the multi-objective alignment problem, particularly in the application of large language models. Traditional methods often require separate fine-tuning for each objective, which is computationally expensive and inflexible. MAVIS avoids modifying base model weights through dynamic adjustments during inference, reducing computational costs and increasing adaptability.
Technical Contribution
MAVIS's technical contribution lies in its innovative inference-time multi-objective alignment method, avoiding the need for separate fine-tuning for each objective. By introducing value models and a tilting function, MAVIS achieves dynamic objective balancing without altering the base model, extending the achievable Pareto front.
Novelty
MAVIS is the first to achieve multi-objective alignment at inference time without fine-tuning the base model. Compared to existing multi-objective optimization methods, MAVIS offers more flexible objective weight adjustments through the combination of value models.
Limitations
- MAVIS may perform poorly under extreme objective weight configurations as the combination of value models might not capture all objectives' complexities.
- Training value models might be time-consuming, especially on large datasets.
Future Work
Future research could explore optimizing the training process of value models to enhance MAVIS's performance under extreme objective weight configurations. Additionally, applying MAVIS to other types of models and tasks could validate its generalizability.
AI Executive Summary
MAVIS is an innovative multi-objective alignment method designed to tackle the challenges faced by large language models in multi-objective tasks. Traditional methods often require separate fine-tuning for each objective, which is not only computationally expensive but also lacks flexibility. MAVIS achieves dynamic multi-objective alignment during inference via value-guided selection, without modifying the base model's weights.
The core of MAVIS lies in training a set of small value models, each corresponding to a specific objective. During inference, these models are combined based on user-specified weights to produce a tilting function that adjusts the base model's output distribution. This allows MAVIS to dynamically balance multiple objectives without altering the base model.
Experimental results show that MAVIS outperforms traditional methods across several benchmarks, particularly on the HH-RLHF and Summarize from Feedback datasets, where MAVIS's Pareto front surpasses that of existing methods. This indicates that MAVIS is not only significant in academia but also provides an efficient multi-objective alignment solution for the industry. While MAVIS may underperform in certain extreme configurations, its flexibility and efficiency make it a significant advancement in the field of multi-objective alignment.
Deep Analysis
Background
In recent years, large language models have made significant progress in natural language processing tasks. However, as application scenarios diversify, models need to satisfy multiple objectives simultaneously, such as helpfulness, harmlessness, and humor. These objectives are often conflicting, necessitating a method to flexibly balance them. Traditional single-objective optimization methods, such as Reinforcement Learning from Human Feedback (RLHF), typically optimize for fixed objective weights, lacking flexibility.
Core Problem
The core problem of multi-objective alignment is achieving dynamic balancing of multiple objectives without altering the base model. Traditional methods require separate fine-tuning for each objective, which is computationally expensive and inflexible. Additionally, adapting to new objectives or preference configurations necessitates retraining the model, which is often impractical in real-world applications.
Innovation
MAVIS's core innovation lies in its inference-time multi-objective alignment method. By training a set of small value models, MAVIS can combine these models at inference based on user-specified weights to produce a tilting function that adjusts the base model's output distribution. This method avoids the need for fine-tuning the base model, making MAVIS computationally efficient and capable of quickly adapting to new objective configurations.
Methodology
- �� Train value models: Train a small value model for each objective.
- �� Combine value models: At inference, combine these value models based on user-specified weights.
- �� Generate tilting function: Use the combined value models to generate a tilting function.
- �� Adjust output distribution: Adjust the base model's output distribution using the tilting function to achieve multi-objective alignment.
Experiments
Experiments were conducted on the HH-RLHF, Summarize from Feedback, and PKU-safeRLHF datasets. The Llama-2 and Alpaca models were used as base models to evaluate MAVIS's performance under different objective weight configurations. The experimental design included comparisons with traditional fine-tuning methods and performance evaluations across different model scales.
Results
Experimental results show that MAVIS outperforms traditional methods across several benchmarks, particularly on the HH-RLHF dataset, where MAVIS's Pareto front significantly expands. Additionally, MAVIS can quickly adapt to new objective weight configurations without retraining the model.
Applications
MAVIS can be applied to natural language processing tasks requiring multi-objective balancing, such as dialogue generation, text summarization, and content filtering. Its flexibility makes it suitable for scenarios requiring dynamic objective weight adjustments, such as personalized recommendations and content moderation.
Limitations & Outlook
While MAVIS performs well across several benchmarks, it may underperform under extreme objective weight configurations. Additionally, training value models might be time-consuming, especially on large datasets. Future research could explore optimizing the training process of value models to enhance MAVIS's performance.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and you need to satisfy multiple requirements like taste, health, and speed. MAVIS is like a smart assistant that can dynamically adjust the proportions of ingredients based on your preferences without needing you to relearn how to cook. This way, you can quickly adapt to different dietary needs without changing the base recipe.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to complete multiple tasks like collecting coins, defeating enemies, and unlocking new levels. MAVIS is like a super assistant that can dynamically adjust your game character's abilities based on your strategy without needing you to restart the game. This way, you can quickly adapt to different game challenges without changing the game rules.
Glossary
Pareto Front
In multi-objective optimization, the Pareto front is the set of solutions that cannot be improved in any objective without degrading another.
MAVIS extends the Pareto front, demonstrating its advantage in multi-objective alignment.
KL Regularization
KL regularization is a method to control model deviation by penalizing the difference between model output distribution and reference distribution.
MAVIS uses KL regularization to ensure model outputs do not deviate from the base model.
Value Model
A value model evaluates the value of taking a particular action in a given state, often used to guide decision-making.
MAVIS achieves multi-objective alignment by training value models.
Tilting Function
A tilting function is used to balance objective weights by adjusting the output distribution.
MAVIS uses a tilting function during inference to adjust the base model's output.
Multi-objective Optimization
Multi-objective optimization involves finding optimal solutions while simultaneously optimizing multiple objectives.
MAVIS achieves dynamic balancing of multiple objectives through multi-objective optimization.
Open Questions Unanswered questions from this research
- 1 How to improve MAVIS's performance under extreme objective weight configurations? Current methods may not capture all objectives' complexities.
- 2 How to enhance the training efficiency of value models? Training time might be long, especially on large datasets.
Applications
Immediate Applications
Dialogue Generation
MAVIS can be used to generate dialogue content that aligns with user preferences, suitable for customer service and chatbots.
Text Summarization
MAVIS can generate text summaries that meet specific objectives, such as news summaries and academic paper summaries.
Long-term Vision
Personalized Recommendation
MAVIS can dynamically adjust recommended content based on user preferences, enhancing user satisfaction and engagement.
Abstract
Large Language Models (LLMs) are increasingly deployed across diverse applications that demand balancing multiple, often conflicting, objectives -- such as helpfulness, harmlessness, or humor. Many traditional methods for aligning outputs to user-specific preferences require fine-tuning models for each objective or for specific preference configurations, which is computationally expensive and inflexible. We introduce \textbf{MAVIS} -- \textit{Multi-Objective Alignment via Inference-Time Value-Guided Selection} -- a lightweight inference-time alignment framework that enables dynamic control over LLM behavior without modifying the base model's weights. MAVIS trains a set of small value models, each corresponding to a distinct objective. At inference time, these value models are combined using user-specified weights to produce a tilting function that adjusts the base model's output distribution toward desired trade-offs. The value models are trained using a simple iterative algorithm that enables monotonic improvement of the KL-regularized policy. We show empirically that MAVIS achieves a superior pareto front compared to baselines which fine-tune per-objective models and combine them post hoc or train a single preference-conditioned value model for guidance. Our code is available at https://github.com/5-Jeremy/MAVIS/tree/main.