Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization

2025-11-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Life Sciences & Biology · Depth: Expert, extended

Summary

This paper introduces Smooth Tchebysheff Optimization of Multi-Objective Preferences (STOMP), a novel offline reinforcement learning (RL) algorithm designed for aligning large language models with multiple, often conflicting, human preferences. Unlike prior methods that rely on linear reward scalarization, which fails to recover non-convex regions of the Pareto front, STOMP frames multi-objective RL itself as an optimization problem to be scalarized using smooth Tchebysheff scalarization. This approach dynamically standardizes individual rewards based on observed distributions, circumventing per-reward scaling hyperparameters. The authors empirically validated STOMP on protein engineering tasks, aligning three autoregressive protein language models (ProGen3-3B, ProGen-RA-3B, ProGen-RA-10B) across three laboratory datasets (DHFR, PbrR, α-Amylase). STOMP achieved the highest hypervolumes in eight of nine settings in both offline off-policy and generative evaluations, demonstrating its robustness and superior performance compared to state-of-the-art baselines.

Key takeaway

For AI Scientists and Machine Learning Engineers working on multi-objective alignment tasks, STOMP offers a robust solution to overcome the limitations of linear scalarization. Your teams should consider integrating STOMP, particularly for applications like protein engineering or chatbot development, where optimizing conflicting objectives is critical. This method's ability to recover non-convex Pareto fronts can lead to more effective and nuanced model performance, improving the quality of generated outputs across multiple metrics.

Key insights

STOMP uses smooth Tchebysheff scalarization for multi-objective offline RL, outperforming linear methods by recovering full Pareto fronts.

Principles

• Linear scalarization fails for non-convex Pareto fronts.
• Dynamic reward standardization improves multi-objective optimization.
• Hypervolume is a key metric for multi-objective performance.

Method

STOMP extends direct preference optimization by applying smooth Tchebysheff scalarization to the multi-objective RL problem, dynamically standardizing rewards based on observed distributions in an offline dataset to derive a policy-independent scalarized reward.

In practice

• Apply STOMP for multi-attribute protein optimization.
• Consider STOMP for multi-objective chatbot alignment.
• Use STOMP for text-to-image generation with multiple objectives.

Topics

• Pareto-Optimal Reinforcement Learning
• Smooth Tchebysheff Scalarization
• Multi-Objective Optimization
• Offline Reinforcement Learning
• Direct Preference Optimization

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.