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arxiv: 2605.00384 · v1 · submitted 2026-05-01 · 💻 cs.RO

PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

Ziqin Yuan , Ruiqi Wang , Dezhong Zhao , Baijian Yang , Byung-Cheol Min This is my paper

Pith reviewed 2026-05-09 19:37 UTC · model grok-4.3

classification 💻 cs.RO
keywords preference-based reinforcement learningmixture of expertsreward learningnoisy preferencestrajectory routingrobust modelingpolicy optimization
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The pith

A mixture-of-experts reward model with trajectory-level soft routing captures diverse latent preferences better than a single averaged model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Preference datasets collected from humans or synthetic sources contain conflicting signals due to annotator disagreements and internal inconsistencies. Single reward models trained on such data must average incompatible signals and lose accuracy as a result. PrefMoE instead maintains multiple specialized reward experts and combines their outputs adaptively for each full trajectory via soft routing weights. A load-balancing regularizer keeps the experts from collapsing to identical behavior. On locomotion and manipulation benchmarks the resulting reward models produce more accurate preference predictions and support more reliable downstream policy optimization.

Core claim

PrefMoE learns multiple specialized reward experts and uses trajectory-level soft routing to combine them adaptively, enabling the model to capture diverse latent preference patterns under noisy and heterogeneous preference supervision. A load-balancing regularizer further stabilizes training by preventing expert collapse.

What carries the argument

Mixture-of-experts reward architecture with soft trajectory-level routing, where a gating network produces per-trajectory weights over the expert reward heads.

Load-bearing premise

Conflicting preferences in the data arise from a small number of distinct latent patterns that separate expert models can capture without the routing mechanism collapsing.

What would settle it

On a preference dataset engineered to have fully random conflicts with no latent structure, PrefMoE would show no improvement over a single-model baseline in either prediction accuracy or downstream policy performance.

Figures

Figures reproduced from arXiv: 2605.00384 by Baijian Yang, Byung-Cheol Min, Dezhong Zhao, Ruiqi Wang, Ziqin Yuan.

Figure 1
Figure 1. Figure 1 view at source ↗
Figure 2
Figure 2. Figure 2: Overview of PrefMoE. A trajectory σ is first decoupled into state and action streams (s1:t) and (a1:t), which are independently processed by shared intra-modal encoders. The resulting representations are pooled into a context vector, from which a two-layer MLP soft router produces K routing weights g(σ). Each of the K expert inter-modal encoders computes a state–action cross-attention reward sequence; the … view at source ↗
Figure 4
Figure 4. Figure 4: visualizes both the absolute scores and the relative performance retention across ∆p levels. Low additional noise (∆p ≤ +0.1): PrefMoE and PrefMMT dominate. Under the crowdsourced baseline (∆p= +0), PrefMoE leads RIME-offline by 17 and PrefMMT by 10, suggesting that temporal sequence modeling provides the primary advantage when additional synthetic corruption is absent. High additional noise (∆p ≥ +0.2): R… view at source ↗
Figure 5
Figure 5. Figure 5: Effect of annotator pool size on D4RL Gym-average score. All conditions use the same total number of preference pairs. Shaded bands show ±std over five seeds. The annotated ∆ values mark the PrefMoE– PrefMMT gap at Nann = 10 and Nann = 100. PrefMoE is nearly insensitive to pool size, while PrefMMT degrades sharply as diversity grows. MR and RIME-offline degrade modestly. pulled in competing directions and … view at source ↗
read the original abstract

Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale preference datasets, whether collected from crowdsourced annotators or generated by synthetic teachers, often contain heterogeneous and partially conflicting supervision, including disagreement across annotators and inconsistency within annotators. Existing reward learning methods typically fit a single reward model to such data, forcing it to average incompatible signals and thereby limiting robustness. To solve this, we propose PrefMoE, a mixture-of-experts reward learning framework for robust preference modeling. PrefMoE learns multiple specialized reward experts and uses trajectory-level soft routing to combine them adaptively, enabling the model to capture diverse latent preference patterns under noisy and heterogeneous preference supervision. A load-balancing regularizer further stabilizes training by preventing expert collapse. Across locomotion benchmarks from D4RL and manipulation tasks from MetaWorld, PrefMoE improves preference prediction robustness and leads to more reliable downstream policy learning than strong single-model baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces PrefMoE, a mixture-of-experts reward learning framework for preference-based reinforcement learning. It learns multiple specialized reward experts combined through trajectory-level soft routing and employs a load-balancing regularizer to prevent expert collapse. The central claim is that this setup captures diverse latent preference patterns in noisy and heterogeneous data more effectively than single reward models, resulting in better preference prediction and downstream policy performance on D4RL locomotion and MetaWorld manipulation tasks.

Significance. If the empirical claims hold, the work has moderate significance for the field of preference-based RL. It provides a practical way to handle the common problem of conflicting preferences in large datasets without simply averaging them out. The trajectory-level routing is a reasonable extension of MoE ideas to RL trajectories, and the load-balancing term is a standard but useful addition. This could lead to more robust reward models in applications involving human feedback.

major comments (2)
  1. [§3.2 (Routing Mechanism)] §3.2 (Routing Mechanism): The trajectory-level soft routing is presented as key to capturing latent patterns, but the manuscript does not include an analysis or ablation showing that trajectory-level is superior to per-timestep routing or that the soft weights indeed correspond to distinct preference clusters in the data.
  2. [§4 (Experimental Results)] §4 (Experimental Results): The benchmark improvements are reported without accompanying ablations on the number of experts or the load-balancing coefficient, which are free parameters in the model; this weakens the ability to attribute gains specifically to the proposed mixture structure.
minor comments (2)
  1. [Abstract] Abstract: The abstract claims 'improves preference prediction robustness' but does not provide any numerical values or specific metrics, which would help readers assess the magnitude of the improvement.
  2. [Related Work] Related Work: Consider adding a reference to recent works on handling noisy preferences in RL, such as those using uncertainty estimation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's significance. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: §3.2 (Routing Mechanism): The trajectory-level soft routing is presented as key to capturing latent patterns, but the manuscript does not include an analysis or ablation showing that trajectory-level is superior to per-timestep routing or that the soft weights indeed correspond to distinct preference clusters in the data.

    Authors: Trajectory-level routing is motivated by the fact that preference labels in PbRL are elicited over full trajectories, allowing capture of coherent patterns across sequences. We agree that explicit validation is needed. In the revised version we will add an ablation comparing trajectory-level soft routing to a per-timestep variant, together with analysis (expert activation heatmaps and clustering metrics on preference types) showing that the learned weights align with distinct latent clusters. revision: yes

  2. Referee: §4 (Experimental Results): The benchmark improvements are reported without accompanying ablations on the number of experts or the load-balancing coefficient, which are free parameters in the model; this weakens the ability to attribute gains specifically to the proposed mixture structure.

    Authors: We acknowledge that sensitivity analysis on these hyperparameters would better isolate the contribution of the mixture structure. The reported results use a configuration chosen after preliminary tuning. The revision will include ablations varying the number of experts (1–8) and the load-balancing coefficient, reporting performance trends on the D4RL and MetaWorld benchmarks. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes PrefMoE as a new mixture-of-experts reward learning framework that learns specialized experts combined via trajectory-level soft routing plus a standard load-balancing regularizer. No equations, derivations, or first-principles results are shown that reduce the claimed robustness or downstream policy improvements to quantities fitted from the same data by construction. The central modeling assumption (that heterogeneous preferences arise from a small number of latent patterns capturable by MoE) is presented as an empirical hypothesis rather than a self-definitional identity, and no self-citation chains, uniqueness theorems, or ansatzes smuggled via prior work appear load-bearing in the provided text. The derivation is therefore self-contained as an architectural proposal whose success is evaluated on external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

Based solely on the abstract; concrete parameter counts, routing architecture, and regularization strength are not specified.

free parameters (2)
  • number of experts
    The mixture size is a design choice that must be selected for the framework to function.
  • load-balancing coefficient
    The strength of the regularizer that prevents expert collapse is a tunable hyperparameter.
axioms (1)
  • domain assumption Heterogeneous preference data contains identifiable latent patterns suitable for mixture modeling
    This assumption justifies replacing a single reward model with multiple experts.
invented entities (1)
  • trajectory-level soft routing no independent evidence
    purpose: To adaptively weight expert contributions based on full trajectory context
    New mechanism introduced to handle diversity without hard assignment.

pith-pipeline@v0.9.0 · 5478 in / 1259 out tokens · 42518 ms · 2026-05-09T19:37:34.583502+00:00 · methodology

discussion (0)

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Reference graph

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