Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment

Renxuan Tan; Rongpeng Li; Zhifeng Zhao; Honggang Zhang

cs.AIApr 7, 2026

Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment

Authors: Renxuan Tan, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

Organizations: College of Information Science and Electronic Engineering, Zhejiang University · Zhejiang Lab, Hangzhou, China · Macau University of Science and Technology, Macau, China

Abstract

Transcending the single-preference paradigm, aligning LLMs with diverse human values is pivotal for robust deployment. Contemporary Multi-Objective Preference Alignment (MPA) approaches predominantly rely on static linear scalarization or rigid gradient projection to navigate these trade-offs. However, by enforcing strict conflict avoidance or simultaneous descent, these paradigms often prematurely converge to local stationary points. While mathematically stable, these points represent a conservative compromise where the model sacrifices potential global Pareto improvements to avoid transient local trade-offs. To break this deadlock, we propose Pareto-Lenient Consensus (PLC), a game-theoretic framework that reimagines alignment as a dynamic negotiation process. Unlike rigid approaches, PLC introduces consensus-driven lenient gradient rectification, which dynamically tolerates local degradation provided there is a sufficient dominant coalition surplus, thereby empowering the optimization trajectory to escape local suboptimal equilibrium and explore the distal Pareto-optimal frontier. Theoretical analysis validates PLC can facilitate stalemate escape and asymptotically converge to a Pareto consensus equilibrium. Moreover, extensive experiments show that PLC surpasses baselines in both fixed-preference alignment and global Pareto frontier quality. This work highlights the potential of negotiation-driven alignment as a promising avenue for MPA. Our codes are available at https://anonymous.4open.science/r/aaa-6BB8.

Figures & tables

Figure 1: Overview of the Pareto Lenient Consensus (PLC) framework for multi-preference LLM alignment. Unlike baselines that get trapped in a suboptimal risk-averse equilibrium due to early gradient stalemate ( $\mathbf{0}\in\rm{Conv}\{\nabla J_{k}\}$ ), PLC leverages coalition-based lenient filtering to rectify update directions, successfully navigating towards the Pareto frontier.

		LLM-as-a-Judge			Proxy Reward Models
Model Size	Algorithm	Helpful (R0)	Harmless (R1)	Avg	Helpful (R0)	Harmless (R1)	Avg
8B	SOLO-R0	$3.57\pm 2.25$	$8.71\pm 1.66$	$6.13\pm 3.24$	$-0.35\pm 1.85$	$1.12\pm 1.42$	$0.39\pm 1.81$
	SOLO-R1	$5.79\pm 1.95$	$8.47\pm 2.78$	$7.13\pm 2.75$	$1.91\pm 1.30$	$-1.23\pm 1.31$	$0.44\pm 2.12$
	RS	$4.03\pm 2.22$	$8.33\pm 2.42$	$6.18\pm 3.17$	$-0.68\pm 1.63$	$-0.09\pm 1.38$	$0.29\pm 1.55$
	GAPO	$5.18\pm 2.01$	$8.87\pm 2.06$	$7.03\pm 2.75$	$1.72\pm 1.78$	$-0.33\pm 1.47$	$0.75\pm 2.00$
	Ric	$4.41\pm 2.26$	$8.56\pm 2.29$	$6.49\pm 3.08$	$1.42\pm 1.46$	$-0.10\pm 1.38$	$0.66\pm 1.61$

Table 1: Main results on the Anthropic-hh-rlhf dataset using Llama-3.1-8B and Llama-3.1-1B . We report scores (Mean

\pm

std across test instances) from both proxy reward models and LLM-as-a-Judge to provide a comprehensive evaluation under balanced preferences for helpful and harmless. Bold indicates the best performance.

Figure 2: (a) Comparison of Pareto frontiers; (b) LLM-judge scoring under equal preference. Evaluation conducted on Harmless and Helpful preferences with Anthropic-hh-rlhf .

Figure 3: PLC against other baselines across different multi-objective metrics. Evaluation conducted on Harmless and Helpful preferences with Anthropic-hh-rlhf .

Figure 4: (a) Comparison of Pareto frontiers; (b) LLM-judge scoring under equal preference. Evaluation conducted on Humor and Helpful preferences with Anthropic-hh-rlhf .

Figure 5: PLC against other baselines across different multi-objective metrics. Evaluation conducted on Humor and Helpful with Anthropic-hh-rlhf .

Figure 6: Navigating tri-dimensional trade-offs on the Anthropic-hh-rlhf . ( Left ) Visualization of the 3D Pareto manifold. ( Right ) We analyze the trade-offs among Harmless (Blue), Humor (Yellow), and Helpful (Red) across diverse preference configurations. The x-axis groups correspond to $\mathbf{w}=(w_{\rm harm}|w_{\rm humor}|w_{\rm help})$ listed on the right, ordered from top to bottom respectively. Labels: a : PLC (Ours), b : GAPO, c : RiC, d : RS, e-g : SOLO variants (Uniform only). Scores are min-max normalized to $[0.1,1.1]$ for visualization purposes.

Figure 7: (a) Comparison of Pareto frontiers; (b) LLM-judge scoring under equal preference. Evaluation conducted on Harmless and Helpful preferences with BeaverTails .

Figure 8: PLC against other baselines across different multi-objective metrics. Evaluation conducted on Humor and Helpful preferences with BeaverTails .

Figure 9: Ablation study on the impact of lenient filtering on training dynamics. Experiments are conducted on Anthropic-hh-rlhf with Harmless v.s. Helpful.

$\tau$	$0.01$	$0.05$	$0.5$	$1$	$5$
Humor	$0.97$	$1.69$	$1.15$	$1.46$	$0.27$
Helpful	$1.71$	$0.83$	$0.51$	$0.92$	$1.50$
Avg.	$2.68$	$2.72$	$1.66$	$2.38$	$1.77$

Table 2: The effect of hyperparameter

\tau

for PLC. We test Humor v.s. Helpful on the Anthropic-hh-rlhf .

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

General Architecture & LoRA Configuration
Hyperparameter	Value	Hyperparameter	Value
Base Model	Llama-3.1-8B	LoRA Rank ( $r$ )	64
Precision	bfloat16	LoRA Alpha ( $\alpha$ )	128
Optimizer	Adam	LoRA Dropout	0.05
LR Scheduler	Cosine	LoRA Bias	None
Warmup Steps	20	Task Type	CAUSAL_LM

Appendix

Table 3: Detailed implementation specifications and hyperparameters. We report the specific settings for the base model architecture, the PLC dynamics, PPO optimization (also the configuration parameter for the RS baseline), and the open-source reward models and datasets used in our experiments.

RiC: Offline & Online Configuration
Hyperparameter	Value
Offline Finetuning Steps	20,000
Initial Learning Rate (Offline)	$1\times 10^{-5}$
Learning Rate Scheduler (Offline)	Linear
Online Iterations	1
Learning Rate Scheduler (Online)	Constant

Appendix

Table 4: Hyperparameter settings for the RiC baseline.

GAPO Configurations
Parameter	Value	Parameter	Value
Base Epochs	2	Max Length	512
Actor LR	$2.50\times 10^{-6}$	Gradient Est.	Last LoRA Layer
LR Scheduler	Cosine	Warmup Ratio	0.03
Weight Decay	0.0	Gradient Clip	0.5
Train Batch Size	16	Gradient Norm	$p=1$

Appendix

Table 5: Hyperparameter settings for GAPO baseline.

Figure 10: Reward distribution dynamics under varying preference weights. The histograms illustrate the shift in reward values for harmless (Score 1) and helpful (Score 2) on Anthropic-hh-rlhf . As the weight vector varies from

(0.1,0.9)

(0.9,0.1)

, the clear directional shift of the distributions corresponding to the weight changes validates the effectiveness of PLC, exhibiting fine-grained controllability and precise alignment with user-defined preference priorities.

Figure 11: Performance comparison under noisy reward conditions. We evaluate the average reward of PLC and Linear Scalarization (LS) on the Anthropic-hh-rlhf dataset, where varying intensities of noise are injected into

30\%

of the training batch for both harmless and helpful objectives.

Figure 12: Quantitative assessment of tri-objective alignment (Harmless, Humor, Helpful) on the Anthropic-hh-rlhf dataset. We report multi-objective performance metrics including Hypervolume (

\uparrow

), Max Spread (

\uparrow

), Preference Compliance (

\uparrow

), and Inverted Generational Distance (IGD,

\downarrow

). PLC (red) significantly outperforms baselines, particularly in Hypervolume, indicating that it successfully expands the volume of the solution space in this high-dimensional setting without collapsing into single-mode optima.

Figure 13: Pairwise 2D projections of the tri-objective (Harmless, Humor, Helpful) Pareto frontier on the Anthropic-hh-rlhf dataset. We project the 3D solutions onto three planes: (a) Harmless vs. Humor, (b) Harmless vs. Helpful, and (c) Humor vs. Helpful. To ensure visual clarity and effectively delineate the frontier boundaries, we plot trajectories formed by a representative subset of preference weight vectors. PLC consistently maintains a superior convex hull (closer to the top-right corner) compared to baselines like GAPO and RiC, which often exhibit sharp performance degradation when trading off conflicting objectives like Humor and Harmless.

Prompt	Human: Should I skip out paying my ticket? Assistant:
Base Model	I don’t think that’s a good idea.
SOLO-Helpful	I’m not sure, but I can try to help you understand the situation. It sounds like you’ve been caught speeding, and you’re now facing a fine and points on your license. If you pay the fine and points, you’ll probably be able to keep your license. If you skip out on paying, you’ll be convicted of a crime, and you’ll lose your license.
SOLO-Harmless	What do you mean ?
RiC	You should always pay your ticket. This is a way you can help the community, and also it can help you.
RS	I’m not sure I understand your question.
GAPO	You have a warrant out for your arrest.

Appendix

Table 6: Case study of different algorithms on Anthropic-hh-rlhf (0.5 Harmless & 0.5 Helpful).

Prompt	Human: What does a flight attendant do? Assistant: You mean a flight attendant on an airplane? He or she is responsible for flight safety, cabin service, and providing a comfortable and safe environment for passengers. They generally work in teams of two to four people, who work eight hours per day, split up into “rotations” so each person gets two off each day. They also take emergency training classes, such as “The Four P’s”, which are Panic, Position, Passengers, and Plastics. “Passengers” means ensuring that passengers aren’t physically hurt and that they’re buckled into their seats safely; “Plastics” means that they make sure that the flotation equipment, such as life jackets, is nearby and ready for use. Assistant: Human: Do they need any special training?
$w_{1}=0.1$	They need to be trained in flight safety, cabin service, and emergency procedures. They also need to be certified by the Federal Aviation Administration.
$w_{1}=0.3$	Flight attendants are required to have a high school diploma, and they usually receive training in customer service, safety, and first aid.
$w_{1}=0.5$	Yes, flight attendants need to complete a flight attendant training program, which is a series of classes that teaches them about safety and service. Flight attendants also need to complete a flight attendant certification program.
$w_{1}=0.6$	Most flight attendants are trained in order to ensure the safety of passengers on flights, and to provide excellent service to passengers. Some of the most important skills necessary for this job are knowledge of how to work emergency procedures, and of how to provide customer service to passengers. Flight attendants also typically have good communication skills, and have experience working with people from different cultures and backgrounds, since they will be interacting with passengers from around the world. Most flight attendants also have 120 hours of training prior to working on an airplane, and additionally take regular refresher courses.
$w_{1}=0.7$	Yes, flight attendants must complete a minimum of 12 weeks of training, and they also have to pass a written exam and a flight test. They must also complete ongoing training classes, which are required every few years. They also need to be certified by the FAA (Federal Aviation Administration). This means that they must pass a written exam, and also complete a flight test. This test is administered by the FAA, and it’s typically done in an airplane.
$w_{1}=0.9$	What do you mean? I’m sorry, I don’t understand your question.

Appendix

Table 7: Case study of PLC with different

w_{i}

configurations on Anthropic-hh-rlhf (Harmless & Helpful).

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