Post-Training: The Critical Path to Unlocking the Potential of Ultra-Large-Scale Parameters

Why Post-Training Has Become the Key Variable for Ultra-Large Models

In recent years, most industry attention has focused on the pre-training side: more compute, more data, more parameters. But two realities are changing that narrative.

First, the law of scale is yielding diminishing returns. As parameter counts move from hundreds of billions to trillions, the capability gain from the same amount of compute becomes increasingly expensive, while the supply of high-quality training data is narrowing. Restarting a larger pre-training run carries order-of-magnitude costs, yet the payoff is not necessarily linear.

Second, there is a massive gap between base capability and usable capability. A trillion-parameter foundation model after pre-training is like a newcomer who has read every book but never held a job: it possesses vast knowledge representation, yet cannot reliably follow instructions, use tools, or stay dependable across long-horizon tasks. Converting “knowledge stock” into “job-ready skills” is exactly the job of post-training.

So the idea of “using post-training to unlock parameter potential” is not marketing rhetoric—it is a clear engineering logic: parameter scale determines the ceiling of capability, while post-training determines how much of that capability is actually realized. When the marginal cost of pre-training rises sharply, post-training becomes the highest-ROI lever for capability growth.

The Post-Training Tech Stack: Four Core Components

Supervised Fine-Tuning: Teach Behavior First, Capability Second

Supervised fine-tuning (SFT) is the first stop in post-training. Its goal is not to inject more knowledge but to shape behavioral formats: instruction following, response structure, refusal boundaries, and output style.

In practice, the data recipe matters more than the sheer volume of data:

• Diversity: Cover different task types, domains, difficulty levels, and response lengths to avoid a lopsided model.
• Difficulty progression: Move from simple Q&A to multi-step reasoning gradually, ensuring a smooth curriculum.
• Quality consistency: Keep answer styles uniform and reasoning chains complete. Fewer but better samples win.
• Forgetting prevention: Mix in a proportion of general data to suppress catastrophic forgetting.

A common anti-pattern is “bulk fine-tuning”—using hundreds of thousands of low-quality, homogeneous samples to fake instruction-following, only to sacrifice generalization and reasoning ability.

Verifiable Reward Reinforcement Learning: Let Right and Wrong Speak

For tasks with objective ground truths—math, code—the industry is increasingly adopting reinforcement learning with verifiable rewards (RLVR). Instead of relying on human preference labels, the output is directly checked by compilers, unit tests, or rule-based validators, and the result serves as the reward signal.

Compared to reinforcement learning from human feedback (RLHF), verifiable rewards offer two significant advantages:

1. Rewards are objective, making it hard for models to game the system with sycophantic answers.
2. They scale automation—reward coverage grows as the task set expands.

A simplified training loop can be illustrated as follows:

# Pseudocode: reinforcement learning with verifiable rewards
for batch in task_stream:                  # Task stream: math problems / coding tasks
    rollouts = policy.sample(batch, n=G)   # Sample multiple candidate solutions per task
    for r in rollouts:
        r.reward = verifier.check(r.output)  # Compilation, unit tests, rule checks
    adv = group_normalize([r.reward for r in rollouts])  # Group-relative advantage
    policy.update(rollouts, adv)           # Update policy using correctness signals only

Key engineering concerns for this approach: the verifier must be strict enough (to prevent passing by luck), the task distribution must be broad enough (to avoid overfitting to question patterns), and the sampling strategy must preserve exploration space (to prevent premature convergence).

Preference Optimization: Handling Tasks That Cannot Be Auto-Verified

Writing, communication, and style tasks have no single correct answer, so they still rely on preference data: either scoring with a reward model followed by RL, or using direct preference optimization methods. This stage determines the subjective “feel” of model usability and is the primary means of aligning safety boundaries.

Multi-Stage Curricula: Align First, Enhance Second

A mature post-training pipeline is rarely a single stage. It is curriculum-based: SFT first to establish baseline behavior and output format, then RL to strengthen reasoning and tool use, followed by progressive training for long context, multimodality, and agentic tasks. The order and ratio of these stages often matter more than any single algorithm.

Long Context: From “Fitting It In” to “Using It Well”

Million-token context windows are becoming the direction for flagship models, but “a large window” and “using it well” are two different things.

On the technical side, long-context capability is typically achieved through staged training: with the support of positional encoding extrapolation, training sequence lengths are expanded gradually from short to long, paired with long-document data curricula, allowing attention distributions to adapt to long-range dependencies.

On the evaluation side, single-point “needle-in-a-haystack” tests are no longer sufficient. Truly discriminative assessments include:

• Cross-document consistency: whether the same fact across multiple sources is cited consistently without contradiction;
• Multi-hop reasoning: answers that must connect distant pieces of information;
• Global structure understanding: chapter-level grasp of long code repositories or lengthy contracts.

On the engineering side, long context puts severe pressure on inference cost and latency. It requires context management strategies, chunked caching, and retrieval augmentation. Not every input deserves to be stuffed into the window—“retrieve what should be retrieved, read closely what should be read closely” is the economical solution.

Native Multimodality and Agents: The New Battlefield for Post-Training

Native Visual Understanding

“Native” means image-text data is deeply interwoven during pre-training, rather than bolted on later as a visual adapter. This makes the model’s understanding of charts, screenshots, and document layouts more robust—visual information is not a “translated footnote” but a representation at the same level as text. In the post-training phase, image-text instruction data is continuously used for alignment, covering tasks such as image Q&A, document parsing, and interface understanding.

Tool Calling and Agentic Capabilities

The core of agentic capability is a closed action loop: understand the task → plan steps → call tools → observe feedback → self-correct. To train such capabilities, the industry commonly uses:

• Trajectory data engineering: collecting high-quality multi-step task trajectories (including failure and recovery samples), filtering, and feeding them back into training;
• Rejection sampling: sampling multiple times for the same task and keeping only successful trajectories for fine-tuning;
• Process supervision: rewarding not just the final result but also the correctness of intermediate steps.

The most representative scenario here is terminal-level real coding tasks: give the model a real command-line environment and let it complete the full loop—“write code → run → read errors → fix → re-verify.” This is fundamentally different from “completing an algorithm problem.” It tests real engineering capability: environment awareness, dependency handling, and long-horizon planning.

Tool calling protocols are also standardizing. A typical function call looks like this:

{
  "name": "run_command",
  "arguments": {
    "cmd": "python -m pytest tests/ -x",
    "timeout": 120,
    "cwd": "/workspace/project"
  }
}

Combined with professional environments—3D modeling tools, CAD software, game engines—models can move from “generating a snippet of code” to “delivering a runnable artifact.” This is also a key observation point in current evaluations that differentiate model tiers.

Evaluation Systems: From Benchmark Chasing to Real Workloads

Academic benchmarks are facing a double bind: saturation (top models are near-perfect on many benchmarks, losing discriminative power) and contamination (test questions leak into training data, inflating scores). As a result, the evaluation trend gaining attention is “real-task-ification”:

• Terminal task evaluation: real engineering tasks in isolated sandboxes, scored by end-to-end pass rates, eliminating “paper talk”;
• Professional document Q&A: real documents such as financial reports, testing table extraction, cross-page citations, and unit conversion;
• Long-horizon agent tasks: multi-step tasks spanning hours, testing stability and self-recovery.

There are widely accepted principles for designing such evaluations: tasks come from real workflows rather than artificial puzzles; execution environments are strictly isolated from training data; evaluations are reproducible and auditable; and dynamic question banks are set up to prevent leakage.

The takeaway for practitioners: don’t rely only on aggregate leaderboards. Build an evaluation matrix that reflects your own business—use real task samples from your domain to measure how the model performs on “your job.”

One Capability, Multiple Deliverables: Inference Efficiency and Open Weights

Flagship models typically need to serve two very different scenarios: interactive dialogue is highly sensitive to time-to-first-token, while batch processing cares more about throughput. The industry’s common approach is “one capability, multiple deliverables”:

• High-speed version: via distillation, quantization, speculative decoding, and other general techniques, inference speed improves severalfold with roughly unchanged capability, suited for interactive scenarios (actual gains depend on model, hardware, and workload characteristics);
• Standard version: aimed at complex tasks and batch processing, balancing capability ceiling and throughput;
• Open weights: full weights released after API validation, supporting private deployment, compliance audits, and domain fine-tuning.

The “API first, weights later” rhythm is also noteworthy: API-first allows rapid collection of real feedback and issue fixes; weights then handed over give enterprises the choice—data never leaves the perimeter, deep customization is possible. This is especially important for highly regulated industries like finance and healthcare.

A Practical Path for Enterprise Adoption

Enterprises looking to convert such model capabilities into business value can plan in layers:

1. Data layer: use object storage for raw corpora and evaluation sets, establish data governance and versioning, and strictly isolate evaluation sets from training data;
2. Training and inference layer: use container platforms and GPU scheduling for training job orchestration, and unified model serving for inference endpoints, canary releases, and rollbacks;
3. Observability layer: use logging services and monitoring alerts to track production quality, automatically feed problematic samples back into the next fine-tuning round, forming a “evaluate → deploy → feed back” loop;
4. Security layer: permission isolation, content safety review, and operation audits are non-negotiable—especially for agentic scenarios with tool execution capabilities.

Using Baidu AI Cloud as an example, its object storage, container platform, model inference service, logging, and monitoring alert capabilities cover the infrastructure requirements across these layers. Enterprises only need to focus on the two areas that truly determine outcomes: data recipes and evaluation matrices.

Conclusion

Ultra-large parameter counts are the “stock”; post-training is the “amplifier.” As the scale race in pre-training enters a high-cost zone, the real competition has already shifted to the systems engineering of post-training: data recipes, reward design, curriculum orchestration, real-task evaluation, inference efficiency, and delivery formats—all tightly linked. For developers, understanding this path is not just understanding a single model release; it is understanding the fundamental paradigm for the next generation of AI capability production—the ceiling is set by parameters, the realization is decided by post-training.