arxiv: 2605.03426 · v1 · submitted 2026-05-05 · 💻 cs.AI

Recognition: unknown

Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models

Shule Lu , Yujing Wang , Hainan Zhang , Xiaoshan Yang , Hongwei Zheng , Yongxin Tong , Changsheng Xu , Zhiming Zheng

Authors on Pith no claims yet

Pith reviewed 2026-05-07 04:00 UTC · model grok-4.3

classification 💻 cs.AI

keywords federated learningvision-language modelspreference alignmentmixture-of-rewardsheterogeneous clientsGRPOreward modelsprivacy preservation

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The pith

A Mixture-of-Rewards router fuses local preference signals so a server can align vision-language models across clients with mismatched architectures and data, without any parameter exchange.

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

Vision-language models are useful in domains that forbid centralizing private data, yet standard federated learning requires clients to share identical model architectures and weights. The paper replaces parameter aggregation with a preference interface: each client trains its own reward model on local preference annotations and shares only that model. A server-side Mixture-of-Rewards component learns to route and blend the incoming reward signals according to the current input and alignment goal. The blended reward then drives GRPO optimization of a shared base model, regularized by a KL penalty to a reference. When this succeeds, the resulting alignment generalizes better across clients than previous federated baselines and works even when client models differ completely in size and structure.

Core claim

The central claim is that preference-based collaboration via per-client reward models and a learned Mixture-of-Rewards router can replace parameter aggregation for federated alignment of heterogeneous vision-language models. Clients keep their data and model architectures private while each trains a local reward model from its preference annotations. The server adaptively fuses these reward signals with input-dependent routing and then optimizes a base VLM using GRPO together with a KL penalty to a reference model. Experiments on diverse public vision-language benchmarks show that this approach yields stronger generalization and cross-client adaptability than existing federated alignment and

What carries the argument

Mixture-of-Rewards with learned routing, which takes outputs from multiple heterogeneous client reward models and produces a single fused reward signal that depends on the input and the alignment objective.

If this is right

Clients can participate even when they use entirely different model architectures and sizes.
Raw data and model weights never leave the client, satisfying privacy constraints in regulated domains.
The same base model can be aligned toward multiple distinct objectives by changing which reward signals the router emphasizes.
Adding or removing clients requires only adding or removing their reward model from the mixture, without retraining the base model from scratch.
Cross-client adaptability improves because the fused reward captures evaluation criteria that no single client possesses.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The routing decisions could be inspected after training to reveal which client signals are most relevant for particular kinds of inputs or tasks.
The same preference-fusion pattern could be applied to federated alignment of other multimodal or text-only models that currently cannot share parameters.
Organizations with proprietary fine-tuned models might use this method to contribute to a shared alignment effort while retaining control over their own weights.
If the router generalizes to new clients, the framework could support dynamic federated cohorts without requiring all participants to be known in advance.

Load-bearing premise

The learned routing inside the Mixture-of-Rewards can reliably decide how much to trust each client's reward signal for any given input without direct access to client data or model internals.

What would settle it

Reproducing the benchmark experiments after introducing deliberately conflicting preferences across clients and finding that the resulting aligned model scores no higher on generalization metrics than the federated baselines that average parameters.

Figures

Figures reproduced from arXiv: 2605.03426 by Changsheng Xu, Hainan Zhang, Hongwei Zheng, Shule Lu, Xiaoshan Yang, Yongxin Tong, Yujing Wang, Zhiming Zheng.

**Figure 1.** Figure 1: Illustration of preference heterogeneity in feder view at source ↗

**Figure 2.** Figure 2: Overview of our proposed federated framework architecture. The framework is composed of three interconnected view at source ↗

**Figure 3.** Figure 3: Scaling performance with more clients on MME view at source ↗

**Figure 5.** Figure 5: Communication overhead comparison across dif view at source ↗

read the original abstract

Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. Federated Learning mitigates this issue by enabling decentralized training, but practical deployments face challenges due to client heterogeneity in computational resources, application requirements, and model architectures. Under extreme model and data heterogeneity, replacing parameter aggregation with preference-based collaboration offers a more suitable interface, as it eliminates the need for direct parameter or data exchange. Motivated by this, we propose MoR, a federated alignment framework that combines GRPO with Mixture-of-Rewards for heterogeneous VLMs. In MoR, each client locally trains a reward model from local preference annotations, capturing specific evaluation signals without exposing raw data. To combine these heterogeneous supervision signals, MoR introduces a Mixture-of-Rewards mechanism with learned routing, which adaptively fuses client reward models according to the input and alignment objective. The server then optimizes a base VLM using GRPO with a KL penalty to a reference model, enabling preference alignment without requiring client models to share architectures or parameters. Experiments on diverse public vision-language benchmarks demonstrate that MoR consistently outperforms federated alignment baselines in generalization and cross-client adaptability. Our approach provides a scalable solution for privacy-preserving alignment of heterogeneous VLMs under federated settings.

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.

Desk Editor's Note private letter to a colleague

MoR aligns one server-side base VLM via routed client rewards and GRPO but leaves unclear how clients with different architectures actually use the result, undercutting the heterogeneous claim.

read the letter

The key point is that MoR has clients train their own reward models on local preferences, then uses a learned Mixture-of-Rewards on the server to combine those signals and run GRPO with a KL penalty to align one base VLM. This setup avoids sharing model parameters or architectures, which is a practical move for privacy-sensitive federated work. The new part is the learned routing over multiple client reward models to handle varying supervision signals. It does a solid job explaining the motivation: when models differ too much, averaging parameters stops making sense, so preferences become the right common language. The soft spots are in the evidence and the scope. The abstract says the method outperforms baselines on public benchmarks for generalization and adaptability, but there are no numbers, no list of baselines, and no ablations to back that up. More critically, the whole thing produces a single aligned base model on the server. The paper's title and setup stress extreme heterogeneity where clients have different architectures and do not share them, yet nothing is said about how those clients would then use the aligned model. If they cannot load it directly, the claimed cross-client benefits do not follow from the method as described. This work is for people studying federated learning with large models or preference optimization in distributed settings. A reader looking for ideas on routing rewards or avoiding parameter exchange would find it relevant. It deserves a serious referee because the core idea is coherent and addresses a real problem, even if the current write-up needs more detail on experiments and on closing the loop with heterogeneous clients. I would recommend sending it to peer review with requests for full experimental results and a clear explanation of how the aligned model reaches clients with mismatched architectures.

Referee Report

3 major / 1 minor

Summary. The paper proposes MoR, a federated alignment framework for heterogeneous vision-language models. Clients locally train reward models from preference annotations to capture client-specific evaluation signals without exposing raw data. A server-side Mixture-of-Rewards mechanism with learned routing adaptively fuses these heterogeneous reward models according to the input and alignment objective. The server then optimizes a single base VLM using GRPO with a KL penalty to a reference model, enabling preference alignment without client models sharing architectures or parameters. Experiments on diverse public vision-language benchmarks are claimed to demonstrate that MoR consistently outperforms federated alignment baselines in generalization and cross-client adaptability.

Significance. If the results hold and the method resolves the gap between server-side base model optimization and alignment of truly heterogeneous client models, the work could be significant for privacy-preserving VLM alignment in federated settings with extreme heterogeneity in resources, data, and architectures. Replacing parameter aggregation with preference-based collaboration via local rewards and learned routing offers a promising interface for domains like healthcare and finance. The combination of Mixture-of-Rewards routing and GRPO is a potentially novel contribution, but the significance is reduced by the apparent mismatch between the stated goals and the described procedure.

major comments (3)

[Abstract] Abstract and method description: The title, motivation, and abstract claim 'alignment of heterogeneous VLMs' under 'extreme model and data heterogeneity' where clients 'do not share architectures.' However, the procedure optimizes only a single server-side base VLM; no mechanism is described for transferring the aligned base model to clients or performing local adaptation when client architectures differ from the base and from each other. This assumption is load-bearing for the cross-client adaptability and generalization claims.
[Experiments] Experiments section: The abstract states that 'MoR consistently outperforms federated alignment baselines in generalization and cross-client adaptability,' yet no details are provided on the baselines, metrics, statistical tests, ablation studies on the routing mechanism, or the specific public benchmarks. Without these, the support for the central outperformance claim cannot be evaluated.
[Method] Method section: The Mixture-of-Rewards with learned routing is presented as fusing client reward models according to input and objective, and GRPO with KL is claimed to produce effective alignment without architecture sharing. However, the absence of equations defining the routing weights, the fusion process, or the GRPO objective makes it impossible to verify whether these components are independent mechanisms or reduce to fitted performance metrics.

minor comments (1)

[Abstract] The abstract would benefit from a high-level pseudocode or key equations for the Mixture-of-Rewards routing and GRPO objective to improve technical clarity and allow readers to follow the contributions without the full method section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. We address each major comment point by point below. Where the manuscript lacked sufficient detail or explicit description, we have revised the text and will incorporate the changes in the next version.

read point-by-point responses

Referee: [Abstract] Abstract and method description: The title, motivation, and abstract claim 'alignment of heterogeneous VLMs' under 'extreme model and data heterogeneity' where clients 'do not share architectures.' However, the procedure optimizes only a single server-side base VLM; no mechanism is described for transferring the aligned base model to clients or performing local adaptation when client architectures differ from the base and from each other. This assumption is load-bearing for the cross-client adaptability and generalization claims.

Authors: We agree that the manuscript would benefit from greater clarity on this point. The central contribution of MoR is to align a single shared base VLM on the server by fusing heterogeneous client reward models via learned routing; this produces an aligned base that encodes the collective preference signals without any client exchanging parameters or architectures. The aligned base is then intended to serve as a common starting point that clients can receive and adapt locally (for example via LoRA or other architecture-agnostic adapters). We acknowledge that the submitted version does not explicitly describe the distribution and local-adaptation step. In the revision we will add a short subsection (new Section 3.4) that details how the server distributes the aligned base checkpoint and how clients with differing architectures can perform lightweight local adaptation while preserving privacy. This addition directly supports the cross-client adaptability claims without altering the core procedure. revision: yes
Referee: [Experiments] Experiments section: The abstract states that 'MoR consistently outperforms federated alignment baselines in generalization and cross-client adaptability,' yet no details are provided on the baselines, metrics, statistical tests, ablation studies on the routing mechanism, or the specific public benchmarks. Without these, the support for the central outperformance claim cannot be evaluated.

Authors: We apologize for the insufficient detail in the initial submission. The experiments section does contain comparisons, but they were not presented with the required granularity. In the revised manuscript we have expanded the section to: (i) explicitly name all baselines (FedAvg, FedProx, and a non-routed reward-averaging variant, all adapted to the VLM setting); (ii) list the evaluation metrics (VQA accuracy, CIDEr, BLEU-4, and human preference win rates); (iii) report statistical significance via paired t-tests with p-values across five random seeds; (iv) add a dedicated ablation table isolating the learned routing component; and (v) specify the exact public benchmarks (VQAv2, OK-VQA, COCO Captions, and a held-out cross-client generalization split). These additions will allow readers to fully evaluate the outperformance claims. revision: yes
Referee: [Method] Method section: The Mixture-of-Rewards with learned routing is presented as fusing client reward models according to input and objective, and GRPO with KL is claimed to produce effective alignment without architecture sharing. However, the absence of equations defining the routing weights, the fusion process, or the GRPO objective makes it impossible to verify whether these components are independent mechanisms or reduce to fitted performance metrics.

Authors: We accept that the absence of formal equations hindered verification. In the revised manuscript we will insert the complete mathematical definitions: the routing weights are obtained via a softmax over a lightweight gating network whose inputs are the current prompt and alignment objective; the fused reward is the convex combination of the individual client reward scores; and the GRPO objective is written explicitly as the policy-gradient term plus the KL penalty to the reference model. These equations will be placed in Section 3.2 and will demonstrate that the routing/fusion layer operates independently of the subsequent GRPO optimization step. The added formalism will make the independence of the components transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity; components presented as independent mechanisms

full rationale

The paper's method is described as a composition of distinct steps: clients train local reward models from preference annotations, a server-side Mixture-of-Rewards with learned routing fuses them adaptively, and GRPO with KL penalty optimizes a single base VLM. No equations, fitted parameters renamed as predictions, or self-citations are shown that reduce any load-bearing claim to its inputs by construction. The experimental claims of outperforming baselines rest on benchmark results rather than tautological redefinitions, making the derivation self-contained against external evaluation.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

Review performed on abstract only; full paper text unavailable so ledger is necessarily incomplete. The approach rests on standard federated-learning and preference-optimization assumptions plus two new mechanisms introduced without independent evidence in the provided text.

free parameters (1)

learned routing weights in Mixture-of-Rewards
Routing parameters are trained to fuse client rewards; their values are not stated and must be fitted during server optimization.

axioms (2)

domain assumption Local preference annotations from each client capture distinct evaluation signals that remain useful when fused without exposing raw data.
Explicitly invoked to justify client-side reward model training.
domain assumption GRPO optimization with KL penalty to a reference model can produce effective preference alignment even when client models differ in architecture.
Central to the server-side step described in the abstract.

invented entities (1)

Mixture-of-Rewards with learned routing no independent evidence
purpose: Adaptively combine heterogeneous client reward models according to input and objective
New component introduced to solve the fusion problem; no external falsifiable prediction supplied in abstract.

pith-pipeline@v0.9.0 · 5554 in / 1651 out tokens · 88881 ms · 2026-05-07T04:00:51.456420+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

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