Evaluating Next-Generation Large Models: Architecture Evolution and the Shift to Post-Training Engineering

Introduction: Capability Gains Are Moving from “Rebuilding the Base” to “Betting on Post-Training”

Over the past two years, a clear consensus has emerged in the industry: the marginal returns from simply adding parameters are diminishing. Instead, “post-training determines the ceiling of real-world capability” has become the dominant narrative for flagship model iteration.

A typical next-generation flagship iteration follows this path: the base architecture largely inherits the previous generation with minimal changes, while compute and engineering resources are concentrated on large-scale post-training and cross-domain joint reinforcement learning. Training, inference, and sandbox-based evaluation are being restructured as a single integrated system.

This shift means the evaluation methodology must also be upgraded. Evaluating a new-generation large model should be split into two layers:

• Base layer: Sparse MoE, million-token context windows, native low-precision weights, and hybrid attention—these determine the capability ceiling and the lower bound on cost.
• Post-training layer: SFT, large-scale reinforcement learning, and sandbox feedback loops—these determine how much of that ceiling is actually realized in real-world tasks.

The rest of this article expands on this framework layer by layer.

1. Sparse MoE and Long Context: The Capability-Cost Trade-off at the Base Layer

Sparse activation is the prerequisite for trillion-parameter deployment

Industry flagship models now reach trillion-parameter scale, but they rely on Mixture-of-Experts (MoE) for sparse activation: each token is routed to only a few experts, keeping the actual activated parameters in the tens of billions. This keeps training and inference compute costs manageable.

When evaluating an MoE model, don’t just look at total parameters. What matters more is:

• Activated parameters per token
• Expert granularity and routing design
• Load balancing across experts

These factors directly determine inference throughput and latency.

Long context has shifted from gimmick to engineering capability

A million-token context window means a model can load an entire medium-to-large code repository or a full business knowledge base in one pass. But the cost of long context is not just “fitting it in memory”: attention computation and KV cache memory grow with sequence length. This is precisely the problem that hybrid attention (discussed later) aims to solve.

The hidden engineering cost of MoE

On the training side, load-balancing loss, expert routing stability, and redundancy between experts all affect convergence quality and inference efficiency. On the deployment side, uneven traffic across experts can create “hot expert” bottlenecks. These issues rarely show up on public leaderboards, but they determine real user experience.

2. Native Low-Precision Weights: Rewriting the Inference Cost Curve with MXFP4

What is MXFP4?

Microscaling FP4 (MXFP4) is a low-precision quantization format defined by industry standards bodies. It applies fine-grained scaling along block dimensions, preserving more detail in weight distributions than per-tensor INT4 quantization. At the same compression ratio, it results in lower quality loss.

Storage and distribution estimates

“Native” low precision means the model is saved as 4-bit during training, rather than trained at higher precision and converted afterward. Here is a rough estimate:

# Rough weight storage estimate (illustrative)
total_params = 2.8e12     # assume 2.8 trillion total parameters
bits = 4                  # MXFP4 weight bit-width
storage_gb = total_params * bits / 8 / 1e9   # ≈ 1400 GB, about 1.4 TB
shards = 96               # split into 96 shards for multi-machine distribution
per_shard = storage_gb / shards              # about 15 GB per shard

Even at 4-bit storage, a trillion-parameter model still requires TB-scale weight files, needing dozens to over a hundred shards distributed across machines. If stored in BF16, the footprint would be four times larger.

This leads to a direct conclusion: the weight distribution pipeline (object storage throughput, multi-NIC concurrency, local NVMe caching) is now one of the hard bottlenecks in large-model deployment.

Operator maturity and quality validation

Under long-context conditions, the maturity of W4-class mixed-precision operators directly determines effective throughput. Native low-precision weights also need dual validation through perplexity and downstream tasks to confirm that accuracy loss is acceptable. This is the “invisible engineering” most easily overlooked in evaluation.

3. Hybrid Attention and Multi-Token Prediction: Two Key Points for Long Context and Throughput

The trade-off of linear attention

Linear attention reduces sequence complexity from quadratic to linear, keeping KV cache memory manageable even at million-token context—this is the prerequisite for commercially viable long context. However, pure linear attention suffers from natural degradation on precision-sensitive retrieval tasks, such as verbatim recall of key-value pairs from a document. It cannot shoulder all attention duties on its own.

Hybrid attention closes the accuracy gap

The mature industry approach is a “linear attention + sparse attention” mix: most segments pass through the linear path to save memory and boost throughput, while sparse attention handles precision-sensitive local regions. The objective of hybrid design is to balance memory cost, throughput, and accuracy simultaneously under long context.

Multi-Token Prediction (MTP)

During training, multiple auxiliary prediction heads are built alongside the main head, forcing the model to plan ahead for subsequent tokens. During inference, these auxiliary heads can be used for speculative decoding with parallel validation. This can multiply decoding throughput severalfold at the same capability level—this is the core technology behind reported “2–3x speedup with no capability loss” iterations.

When evaluating models, throughput should be a first-class dimension: a model that is equally capable but faster is often more valuable for business than one that is slightly stronger but slower.

4. Post-Training and Cross-Domain Joint Reinforcement Learning: The Main Battlefield for Capability Gains

Why the industry is betting on post-training

The base model provides the knowledge foundation, but instruction following, tool invocation, multi-step planning, format stability, and safety boundaries are all shaped by post-training. In code and math domains, the current mainstream approach is to use rule-based “verifiable rewards” (e.g., unit tests passing, answer matching) instead of human preference labeling. This reduces the difficulty of reward modeling and suppresses reward hacking.

Balancing act in cross-domain joint RL

Single-domain reinforcement learning often creates a “capability seesaw”: math improves while general dialogue degrades. To simultaneously improve code, math, tool use, and conversational quality, cross-domain joint training is needed, which requires solving three engineering details:

• Reward normalization across domains
• Sampling ratio between domains
• Curriculum difficulty design

Whether cross-domain training is “truly joint” is the dividing line between shallow and deep post-training.

The three-piece full-stack engineering set

1. Large-scale Rollout: Asynchronous sampling decoupled from training. The sampling throughput of agent trajectories directly limits RL iteration speed.
2. Sandbox Environment: Terminal, file system, restricted network, and code execution containers give the model real feedback instead of static answers.
3. Cheating-Resistant Evaluation: A held-out test set and a dynamic environment prevent the model from overfitting to public leaderboards.

Reference implementation of reward design (pseudocode):

def compute_reward(trajectory):
    r = 0.0
    if tests_pass(trajectory.final_repo):     # unit/regression tests passed
        r += 1.0
    if format_valid(trajectory.final_answer):  # output format is valid
        r += 0.2
    r -= 0.1 * trajectory.step_overrun         # penalty for exceeding step budget
    return r

5. A More Reliable Evaluation System: From Leaderboards to Real Tasks

Terminal/agent-style benchmarks are the most convincing

Evaluate “real task completion”: building a project from scratch in a sandbox, reproducing a bug, fixing it, and passing regression tests. According to public evaluations, completion rates for top models on such real-environment tasks have risen from roughly 50% in the previous generation to around 66%, and the gap between tiers is widening rather than narrowing. This indicates that post-training engineering has a significant amplifying effect on capability.

Code capability should be measured by “test pass rate”

Repository-level completion, patch correctness, and regression stability are closer to production reality than single-function completion. An industry-level observation: a 5-point improvement in code benchmarks between generations typically depends on joint optimization of post-training and inference engineering—stacking more base data alone is unlikely to achieve this.

Include speed as an evaluation dimension

Latency-sensitive scenarios (multi-turn agent calls, IDE completion) are highly sensitive to time-to-first-token and tokens-per-second generation rates. Instead of looking at point scores, the “capability-versus-throughput curve” should be plotted.

Normalize everything by cost

Cost per million tokens, GPU utilization, and cache hit rate—improvements from low-precision weights and sparse activation ultimately only matter if they show up on the bill.

6. Enterprise Deployment: An Engineering Checklist for Deployment and Evaluation

Teams planning to put a trillion-parameter sparse MoE model into production should prepare the following in advance (standard cloud infrastructure is sufficient):

• Weight Distribution: High-capacity object storage, local NVMe caching, and multi-machine/multi-NIC concurrent shard pulling. Cold start should be controlled to within minutes.
• Compute Platform: Containerized GPU scheduling, high-speed interconnect, and observability for multi-machine collective communication.
• Inference Service: KV cache reuse, continuous batching, MTP/speculative decoding acceleration, and monitoring of P95 latency and GPU utilization.
• Evaluation Loop: Build your own business-specific evaluation set, run it periodically in a sandbox, and use real tasks to avoid “inflated leaderboard scores.”

These capabilities can be assembled from general cloud product categories such as bare-metal GPU clusters, object storage, container platforms, and logging/monitoring services. For example, platforms like Baidu AI Cloud support this architecture for weight distribution, service deployment, and evaluation loops—this is also a common pattern in enterprise-level large-model deployment today.

Conclusion: A One-Page Evaluation Checklist

1. Base layer—look at cost structure: activated parameters per token, native low precision, hybrid attention, and MTP support.
2. Post-training layer—look at engineering depth: cross-domain joint RL, sandbox feedback loops, and cheating-resistant evaluation.
3. Delivery form—look at the triangle curve: the balance point among capability, throughput, and cost.
4. For enterprises, the most important factor is your own business evaluation set and deployment pipeline. Leaderboards tell you where the ceiling is; your business evaluation set tells you whether you can actually reach it.