Chain of Experts: What Is It and How It Solves MoE’s Limitations

While large language models (LLMs) demonstrate remarkable abilities in various tasks, their scaling presents significant computational and financial challenges. The scale of a model is key to improving its quality. As shown in the seminal work by Jared Kaplan and Sam McCandlish in their paper, “Scaling Laws for Neural Language Models,” given a fixed compute budget, allocating more resources to model size, rather than to the number of training steps, often results in better performance. In other words, training a larger model for fewer steps is often more beneficial than training a smaller model for more steps. The core challenge in advancing LLMs is finding ways to increase model capability without triggering a massive rise in compute and cost; an essential step toward broader, more sustainable adoption.

Historically, the only way forward was to scale model size directly, until sparse architectures introduced a new path. Mixture of Experts (MoE) models like Mixtral 8x7B and DeepSeek-V2 marked a breakthrough in efficient scaling. By activating only a subset of parameters for each token, MoEs pretrain faster and reduce active compute during inference, without sacrificing model quality. With MoE, teams could train larger models within the same budget constraints as dense architectures.

But MoE has its limits. Expert independence and the lack of inter-expert communication constrain quality, especially in tasks requiring composition or iterative reasoning.

That’s where Chain of Experts (CoE) comes in.

A new architectural paradigm, CoE enables experts to interact sequentially within the same layer by passing signals, refining outputs, and unlocking new forms of compositional depth. Unlike Chain-of-Thought (CoT) prompting, which operates at the input level, CoE modifies the architecture itself. This distinction matters: CoE isn't just about reasoning, it’s about learning more efficiently, with less overhead, and producing higher-quality results per unit of compute.

In this blog, we discuss how CoE improves on MoE, what architectural shifts it introduces, and why it signals the next phase of sparse scaling.

To understand why Chain of Experts (CoE) represents a meaningful architectural step forward, it’s important to examine what Mixture of Experts (MoE) got right, and where it falls short.

MoE recap: Conditional compute as a scaling breakthrough

MoE architectures introduce sparse activation to expand model capacity without a proportional increase in compute. In transformer-based models, this is typically achieved by replacing dense feed-forward (FFN) layers with MoE layers, each containing a set of expert subnetworks and a gating network that routes each input token to a small number of these experts.

For example, in models like Mixtral 8x7B, each MoE layer comprises eight experts. The router selects two experts to process each token, combining their outputs based on learned weights. This structure allows models to access billions of parameters while only activating a small subset for any given input.

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DeepSeek-V3 further underscores this principle: with 671 billion total parameters and 257 experts, it activates only 9 experts, or roughly 37 billion parameters for any given token. This enables performance on par with dense models of similar size while keeping per-token compute significantly lower.

The result: MoE systems can scale efficiently on paper, matching the quality of massive and dense models with far less active computation. But this efficiency introduces four persistent bottlenecks that limit performance, increase complexity, and constrain real-world deployment.

1. Expert isolation: No shared reasoning, limited depth

MoE experts process tokens independently, without any interaction or shared context during inference. The gating network selects a small number of experts per token, and their outputs are combined, but each expert works in isolation.

This lack of coordination restricts the system’s ability to construct rich, layered representations or handle tasks that require iterative reasoning across knowledge domains. As a result, performance suffers on complex, multi-step problems. To compensate, designers often add more layers or increase the total number of experts, introducing additional cost and complexity.

2. Underutilized capacity and high memory overhead

Although only a few experts are activated per token, all expert parameters must be stored in GPU memory to maintain routing flexibility. In Mixtral 8x7B, for instance, inference activates 13 billion parameters, but the full model includes 47 billion due to shared non-expert components.

This leads to two challenges:

• Inefficient parameter use: Most parameters remain idle at runtime.
• High VRAM requirements: All experts must be loaded into memory, even when unused.

DeepSeek-V3’s 257-expert architecture exemplifies this: despite only using 37B parameters per token, the full 671B parameter set must be stored. In effect, MoE trades compute for memory, reducing active floating point operations (FLOPs) while maintaining a bulky, resource-intensive footprint that complicates deployment at scale.

3. Training is unstable, brittle, and hard to tune

MoEs introduce architectural complexity that creates new failure modes during training:

• Load imbalance: Gating networks may over-select certain experts, starving others, and destabilizing learning. Auxiliary loss functions(e.g., Importance and Load loss functions) help, but require careful tuning.
• Router instability: Gating mechanisms are prone to gradient spikes, expert dropout, and logit overflow. Penalties like z-loss mitigate this but add to hyperparameter complexity.
• Lack of specialization: While MoEs aim for expert differentiation, specialization often fails to emerge cleanly. Mixtral 8x7B shows little topic-based routing behavior. Decoder experts often overlap, while encoder experts skew toward surface-level patterns.
• Overfitting during fine-tuning: Sparse models are more sensitive to small datasets. Higher dropout and instruction tuning can help, but require nontrivial calibration.

These issues reflect the challenge of orchestrating a large ensemble of conditionally activated subnetworks, where failure in coordination degrades system-wide performance.

4. The MoE-CAP trade-off: Can’t optimize everything at once

MoE design inherently forces trade-offs across Cost, Accuracy, and Performance (CAP). Models that maximize performance and accuracy typically demand higher memory and computational overhead. Optimizing for cost often results in degraded quality or throughput.

This challenge is compounded by benchmarking gaps. Conventional metrics like Memory Bandwidth Utilization (MBU) and Model FLOPS Utilization (MFU) fail to account for sparse execution, leading to inflated efficiency claims or misaligned comparisons.

In practice, MoEs compromise performance for complex reasoning tasks and impose hard constraints on memory and system integration.

This is the design gap that Chain of Experts aims to address. By rethinking expert interaction and enabling sequential refinement within layers, CoE introduces a new trade space, one where sparsity doesn’t have to mean isolation, and capacity growth doesn’t demand constant compromise.

CoE is an architectural advancement in the sparse model family that addresses Mixture of Experts (MoE) limitations by enabling sequential expert activation with intermediate communication, fundamentally altering how expert modules interact during inference.

While MoE models activate multiple experts in parallel with no interaction, CoE processes tokens through a stepwise pipeline: each expert (or group of experts) builds on the outputs of the previous one. This chain-like structure transforms the expert routing process from a one-shot selection into a dynamic, multi-stage reasoning loop.

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CoE is not a prompting strategy like Chain-of-Thought (CoT); it is a model-level architectural change. Where CoT decomposes reasoning across input-output sequences, CoE embeds compositional reasoning within the model’s internal computation path.

CoE is defined by four interdependent mechanisms that together create a system of communicative, adaptive expert processing:

1. Iterative expert routing

In a CoE layer, input tokens are routed through a series of expert stages. At each stage, a gating network selects a subset of experts based on the evolving token representation. Each expert’s output is then passed to the next stage, refining the token’s hidden state over multiple iterations.

For example, a CoE-2 configuration performs two passes of expert processing per token. Instead of activating 4 experts once, it activates 4 experts twice, each time informed by a more context-rich input. 

The formal representation of this iterative processing is : 

If MoE is a panel of experts voting independently, CoE is a relay team, where each expert receives the baton, refines it, and passes it on.

2. Independent gating per iteration

Unlike MoE, which uses a single routing decision, CoE re-evaluates expert selection at each stage. This dynamic routing allows the model to shift its computation path mid-layer, adapting to the token’s evolving context.

Each stage has its own gating logic, enabling experts to specialize in different phases of reasoning or token transformation. Empirical results show that shared gating across stages degrades performance, highlighting the value of this dynamic adaptivity.

The independent gating mechanism is defined as:

3. Implicit expert communication

Because each iteration receives the output of the previous one, CoE naturally enables implicit communication between experts. This allows the model to construct richer, more context-aware representations, even within a single architectural layer.

Rather than isolated computations aggregated post-hoc, CoE fosters compositional depth through learned interdependencies.

4. Inner residual connections

To maintain stability and effective gradient flow during iterative processing, CoE layers include inner residuals: connections between each stage of expert activation. These allow each expert to learn refinements rather than recomputing the entire transformation from scratch.

Ablation studies confirm that inner residuals (as opposed to outer-only residuals) significantly improve performance and training stability, making them a core architectural requirement.

Why it matters

The CoE framework reframes the idea of layer depth, not by stacking more blocks, but by deepening computation within a block. This yields:

• Greater representational power per parameter
• Better expert utilization and specialization
• Improved reasoning without exploding memory or latency

CoE’s inter-expert dependencies make it a natural fit for tasks that benefit from iterative logic, multi-stage reasoning, or token-wise deliberation. And because CoE maintains sparse activation, it holds promise as a scalable next step in sparse model evolution.

The Chain of Experts framework introduces structural innovations that directly resolve core limitations of Mixture of Experts (MoE) models. The results are tangible: improved accuracy, leaner memory profiles, and better performance on complex reasoning tasks, all without proportional increases in computational cost.

1. Enabling expert collaboration through sequential processing

MoE’s parallel design prevents experts from exchanging information. Each token is processed independently by isolated experts, limiting the model’s ability to reason across multiple steps. CoE addresses this through sequential expert chaining, passing the evolving token representation from one expert to the next within the same layer.

Each iteration allows experts to build upon prior transformations. This chain of computation enables:

• Context-aware refinement: Later experts adjust based on earlier outputs.
• Multi-step reasoning within a single layer: Depth increases without stacking more layers.

This implicit communication unlocks capabilities MoEs struggle with, such as solving mathematical problems that require simplification, transformation, and logical sequencing across steps.

2. Extracting more performance from fewer parameters

CoE significantly improves memory and parameter efficiency by reusing experts iteratively. Experimental results show:

• CoE-2(4/48) matches MoE(8/64) on benchmark tasks while reducing memory usage by ~18%.
• In another case, CoE-2(8/64) with just 4 layers matched the performance of MoE(8/64) with 8 layers, cutting memory requirements by 42%.

By increasing the computational depth per layer, CoE extracts more value from each parameter:

• Fewer total experts, fewer layers
• No loss in performance
• Lower deployment costs

This efficiency makes CoE attractive for resource-constrained or production-scale environments where memory and inference cost are primary considerations.

3. Expanding the expert combination space

One of CoE’s most impactful advantages is combinatorial routing flexibility. Each iteration selects a new set of experts, allowing for exponentially more unique expert interaction paths.

For example:

• MoE(8/64) offers a fixed set of combinations.
• CoE-2(4/64) introduces 823× more unique expert sequences, enabling richer token-specific computation pathways.

This diversity supports better generalization, more nuanced specialization, and tailored token processing, especially for long-tail or high-variance inputs.

4. Strengthening expert specialization and division of labor

In CoE, experts can specialize across both roles and stages:

• An expert may contribute to early simplification in iteration one.
• Another may refine the output in iteration two.
• The same expert can play different roles depending on when and where it is invoked.

This repeated engagement allows CoE to foster niche expert behavior, each expert contributes one “move” in a complex reasoning chain, rather than solving the entire task in isolation.

5. More efficient scaling and a “Free Lunch” in return

MoE scaling typically involves increasing layer depth or expert count, both of which inflate memory and inference costs. CoE, by contrast, scales horizontally through additional iterations, delivering stronger performance without proportional model bloat.

In benchmark evaluations:

• CoE-2(4/64) outperformed MoE(8/64) on math reasoning tasks with the same compute budget.
• Validation loss dropped from 1.20 to 1.12, indicating better accuracy with equal training resources.

These gains, dubbed a “free lunch” acceleration, result from smarter architecture design, not brute-force expansion.

6. Navigating the MoE-CAP trade-off more effectively

Traditional MoEs force tough choices between Cost, Accuracy, and Performance (CAP). CoE shifts that curve by:

• Delivering better performance at the same cost
• Achieving the same accuracy with fewer parameters
• Reducing memory usage without latency penalties

While theoretical TFLOPs may remain constant between CoE and MoE variants, the real-world trade-off is favorable. CoE’s sequential operations may introduce marginal wall-clock overhead, but the return is higher model quality per FLOP and better deployment economics.

While both CoE and MoE rely on sparse activation and specialized expert subnetworks, their underlying architectures and performance characteristics diverge sharply. CoE restructures how experts interact, how depth is expressed, and how computation is routed, unlocking advantages MoE architectures can’t easily match.

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The following table summarizes the key architectural and operational differences:

CoE is not an extension of MoE; it’s a strategic rethinking of expert architecture. By trading breadth for depth and static routing for dynamic sequencing, CoE delivers measurable gains in model efficiency and reasoning quality.

This makes CoE a strong candidate for next-generation AI infrastructure, especially in enterprise settings where both cost-efficiency and high reasoning fidelity are non-negotiable.

The CoE framework offers more than architectural novelty, it delivers measurable business outcomes across high-value, inference-heavy domains. 

High-impact application domains

• Scientific and strategic reasoning: CoE’s multi-step processing mirrors human deduction, enabling better output on tasks like experimental design, complex hypothesis evaluation, and long-term planning.
• Precision-first fields: In healthcare, finance, and legal analysis, CoE's stepwise computation improves interpretability and reduces error, offering safer, more reliable AI assistance.
• On-premise or Edge AI: CoE's reduced memory requirements (17.6%-42%) allow sophisticated models to run on commodity hardware or embedded systems.
• Natural language understanding: Deeper per-layer processing enhances disambiguation, sentiment accuracy, and contextual understanding in nuanced language scenarios.

Business value drivers

• Lower cost of ownership: CoE reduces GPU memory overhead, enabling larger models on existing infrastructure and lowering the per-query cost of inference.
• Unlocking advanced capabilities: Organizations previously constrained by MoE's cost can deploy complex AI features more broadly.
• Faster custom model development: Despite requiring pretraining, CoE's efficiency shortens iteration cycles for building high-performance, domain-specific models.
• Sustainability: By achieving more with fewer resources, CoE supports green computing goals and reduces environmental impact.

The Chain of Experts framework represents a step-change in LLM architecture. By structuring expert interactions around sequential, context-sensitive processing, CoE overcomes the isolation, memory inefficiencies, and rigid routing of traditional MoE systems.

• Efficiency: Up to 42% lower memory use with no loss in performance.
• Performance: Demonstrated improvements on complex reasoning tasks.
• Scalability: More expert combinations, deeper layer utility, and lower compute cost per output.

For enterprise AI leaders, CoE offers a viable path to cost-effective, high-performance model deployment, especially in use cases where stepwise logic, reliability, and inference throughput are mission-critical.

As AI moves from experimentation to scaled infrastructure, CoE stands out not just as a research milestone but as a production-ready design philosophy. The next generation of agentic systems will demand architectures that are not just powerful but also efficient, modular, and compositional. Chain of Experts is a leading candidate to meet that demand.

Talk to a Turing Strategist to explore how CoE-based architectures can accelerate your AI initiatives; more performance, lower cost, and readiness for the agentic era.