The Shift to Post-Training: Unleashing the Potential of Trillion-Parameter LLMs

I. A Noteworthy Industry Signal

Recent capability evolutions in flagship LLMs have sent an intriguing signal: as multiple institutions report significant progress in code generation, agentic tasks, and professional document processing, they emphasize that these leaps do not come from entirely new, larger-scale pre-training. Instead, they stem from systematic iteration during the post-training phase. This means the industry’s competitive focus is shifting from “who can pile up the most parameters and tokens” to “who has the most efficient post-training system.”

An engineering analogy helps clarify their division of labor: pre-training is akin to densely “writing” massive amounts of knowledge into trillion-level parameters, where the model acquires language patterns, world knowledge, and rudimentary reasoning. Post-training, on the other hand, is the “query optimization” of this asset—enabling the model to know which knowledge to retrieve during real tasks, what format to output, and how to plan multi-step actions. The former requires months, incurs high costs, and demands a heavy price for trial and error; the latter allows for rapid iteration, clear signals, and targeted reinforcement across specific capability domains. Pre-training determines the potential ceiling, while post-training dictates the proportion of that potential actually released.

For developers and enterprises, the direct implication of this shift is that evaluating and selecting LLMs based solely on parameter and corpus size is no longer sufficient. Understanding a model’s post-training methodology and evaluation metrics is now the key to determining whether it will perform well in your specific use cases.

II. Why the Marginal Returns of “Piling on Pre-Training” Are Diminishing

1. High-quality data is hitting a ceiling. Pre-training a trillion-parameter base model requires consuming trillions of tokens. High-quality corpora on the public internet have been nearly exhausted after multiple rounds of harvesting. While synthetic data can expand volume, its distribution is often concentrated, and noise is difficult to control. Doubling the data yields far less downstream task improvement than it did during the early stages of expansion.

2. Compute costs and risks are rising non-linearly. The larger the base model, the higher the compute cost for a full pre-training run. The costs of fault recovery and formulation trial-and-error are measured in months. In contrast, a single round of post-training experimentation costs an order of magnitude less, supporting high-frequency iteration and fast failure.

3. Knowledge does not equal capability. The objective function of pre-training is next-token prediction, shaping what the model “knows.” Real-world products, however, test what a model “can do”—understanding complex instructions, calling tools correctly, maintaining long-horizon task states, and strictly adhering to output formats. These behavioral capabilities do not directly align with pre-training objectives and are precisely the main battleground of post-training. This explains why the steepest capability growth curves recently have appeared in the coding and Agent domains: they are the fields with the most abundant “verifiable behaviors.”

III. The Post-Training Technical System: Five Main Pillars

3.1 Supervised Fine-Tuning (SFT): Defining Behavioral Paradigms

SFT uses high-quality instruction-response pairs to teach a model “how to respond” and serves as the starting point for all post-training. The current consensus is that quality is far more important than quantity: tens of thousands of strictly filtered and human-verified samples often outperform a crudely assembled million-scale dataset. Data sources typically blend three types: real user requests (matching the actual distribution), expert-written content (ensuring a high ceiling), and model-generated plus multi-layer filtered data (expanding coverage). Notably, SFT data is no longer limited to text Q&A; an increasing number of teams encode tool-calling trajectories and multi-turn environment interactions directly into training samples:

# High-quality SFT sample structure: includes tool calls and verifiable metadata
sample = {
    "messages": [
        {"role": "system", "content": "You are a coding assistant that can execute commands in a sandboxed terminal."},
        {"role": "user", "content": "The unit tests in the repository are failing. Please locate and fix them."},
        {"role": "assistant", "tool_calls": [
            {"name": "run_tests", "arguments": {"scope": "all"}},
            {"name": "read_file", "arguments": {"path": "src/auth/session.py"}},
            # Subsequent actions depend on the environment output of each step
        ]}
    ],
    "meta": {"domain": "terminal_coding", "difficulty": 3, "verified": True}
}

3.2 Preference Optimization: Aligning “Hard-to-Score” Capabilities

For dimensions like writing style, safety, and response detail that lack a single correct answer, the industry widely adopts methods based on human preference data. Annotators first rank multiple candidate responses, and then preference signals are injected into the policy via a reward model with policy optimization, or through more stable techniques like Direct Preference Optimization (DPO). Its value lies in transforming “what experts think is good” into an optimizable target without needing humans to write standard answers.

3.3 Reinforcement Learning with Verifiable Rewards (RLVR): The Main Engine for Coding and Reasoning

When task outcomes can be automatically judged, the signal quality of reinforcement learning undergoes a qualitative shift. A typical approach involves having the model sample multiple complete solution trajectories in a sandbox environment, calculating rewards based on hard metrics like “whether unit tests pass,” and then updating the policy. The design of the reward function is a core engineering challenge. Beyond the endgame goal, it should also encourage minimal changes, step budgets, and the absence of side effects:

def verifiable_reward(traj) -> float:
    """Reward function based on executable results (illustrative)"""
    r = 0.0
    if traj.env.all_tests_pass():       # End-to-end goal achieved
        r += 1.0
    if traj.diff.is_minimal():          # Minimize the modification footprint
        r += 0.2
    if traj.steps <= traj.budget:       # Completed within the step budget
        r += 0.1
    if traj.env.has_side_effects():     # Penalize destructive operations
        r -= 0.5
    return r

This mechanism explains why coding and Agent capabilities are surging the fastest. Terminal tasks inherently come with objective signals like “tests pass or fail.” RL can automatically acquire training data across massively parallel sandboxes without requiring human annotation for every step.

3.4 Agent and Tool Usage Training: From Single-Turn to Long-Horizon

Enabling a model to stably operate terminals, invoke CAD design tools, or interact with game engines requires specialized multi-turn interaction training: the model outputs a tool call, the environment returns the execution result and state change, and the model adjusts its plan accordingly. Training design universally employs curriculum learning—mastering single-tool, single-step actions first, then expanding to multi-tool combinations, and finally tackling tasks that require dozens of steps over extended periods. Environment error messages themselves are valuable signals; a model that learns to “recover from failure” is one that can truly handle long-duration, multi-step software engineering tasks.

3.5 Long Context and Native Multimodality

Million-token context windows are becoming a crucial capability for flagship models. Their value lies not in “how many words can be stuffed in,” but in allowing an entire prospectus or codebase to enter the working memory at once, reducing the context fragmentation caused by retrieval splicing. Implementation typically requires position encoding extrapolation paired with continued training on long documents. For multimodality, native visual understanding (unified pre-training of visual encoding and language) holds structural advantages over “bolt-on recognition” approaches in chart reasoning, layout comprehension, and cross-modal referencing.

Additionally, there is a parallel speed track: compressing flagship capabilities into smaller student models via knowledge distillation. Combined with inference optimizations like speculative decoding, this yields high-speed versions with “comparable capabilities and several times the throughput.” These handle high-frequency, relatively simple requests, creating a cost tiering strategy alongside the flagship model.

IV. Three Key Battlegrounds for Capability Release

1. Real-world Terminal Coding. Unlike static code completion, terminal tasks require the model to understand the environment, plan commands, parse outputs, and recover from errors. Evaluations must also be end-to-end—scoring based on the final task completion state rather than code snippet similarity. The completion rates of top-tier industry models on these real-world tasks have entered a high range and are still climbing rapidly.

2. Professional Knowledge Work. Document analysis in fields like finance and law tests cross-page referencing, numerical consistency in tables, and long-horizon reasoning. This is a classic scenario combining long context and multimodality: the model must read extensively and comprehend chart structures.

3. Complex Toolchain Operations. 3D modeling, CAD design, and game engine operations require models to maintain external state machines. Tool calls have strict sequential dependencies, and parameter errors trigger cascading failures. This pushes post-training from “teaching models to talk” to “teaching models to do.”

V. Verifying Released Potential: Evaluation System Design

Whether capabilities are truly released depends on your evaluation metrics. Three practical recommendations:

First, end-to-end over static. Multiple-choice benchmarks are easily contaminated by data and fail to reflect the stability of multi-step tasks. Real tasks should be executed in sandboxes, assessing the final state.

Second, look at variance across multiple samples. Agent task outcomes naturally fluctuate; the success or failure of a single run holds no statistical significance. Reports should detail the average completion rate and fluctuation interval across multiple samples:

def evaluate(model, tasks, k=5):
    report = []
    for t in tasks:
        runs = [run_once(model, t, sandbox=fresh_env()) for _ in range(k)]
        report.append({
            "task": t.id,
            "pass_rate": mean(runs),
            "stability": 1 - std(runs),   # High variance = unstable capability
        })
    return aggregate(report)

Third, build private task sets. Aggregate sanitized real tasks from your own business to form a version-controlled private evaluation set. This prevents overfitting and contamination from public benchmarks and serves as the starting point for an enterprise data flywheel.

VI. Engineering Implementation Recommendations

A checklist for teams preparing to integrate these capabilities into their operations:

• Select by capability domain, not aggregate scores. The depth of post-training varies greatly across coding, Agent, and long-document tasks. Testing with your own task sets is far more reliable than public leaderboards.
• Adopt a dual-track model strategy. Route difficult problems to the flagship model and pass high-frequency, simple requests to the distilled high-speed version to balance throughput and quality.
• Focus on open-weight models. If open weights are available, enterprises can perform continued domain-specific post-training, securing data sovereignty and deployment autonomy.
• Build a solid infrastructure foundation. Setting up custom evaluation and post-training pipelines involves GPU compute scheduling, containerized environments, object storage for large-scale trajectory data, and logging/monitoring for training and online services. This can be built using general infrastructure capabilities provided by cloud platforms like Baidu AI Cloud to avoid reinventing the wheel.
• Treat the data flywheel as an asset. The success and failure trajectories of online tasks, once cleaned, are the fuel for the next round of post-training. The rotation speed of this flywheel determines long-term competitiveness.

VII. Conclusion

Parameters and pre-training corpora determine a model’s latent potential, but the post-training system dictates the speed and proportion of that potential’s release. As the industry shifts its focus from “bigger” to “smarter,” the true moat is moving from compute scale to three things: the ability to acquire high-quality preference and verifiable data, a rigorous evaluation system, and the infrastructure to support high-frequency RL iterations. For developers, understanding this paradigm means understanding the main trajectory of LLM evolution over the next year or two. For enterprises, building private evaluation systems and data flywheels early is the only way to secure a share of this capability dividend.