Post-Training: The Key Path to Unlocking the Potential of Trillion-Parameter Large Models

1. From “Piling Parameters” to “Refining Parameters”: The Underlying Logic of Paradigm Shift

In recent years, enhancing large model capabilities relied heavily on expanding pre-training scale: more data, more compute, and more parameters. However, as high-quality public corpora become exhausted and single pre-training costs increase exponentially, the marginal returns of simply scaling pre-training are diminishing. Consequently, the industry is shifting focus: once parameter scales reach a certain magnitude, resources are redirected from “training a larger base model” to “fully unlocking the potential of existing base models.”

A frequently underestimated fact is that many capabilities learned during pre-training remain “latent.” Parameter scale provides the capacity ceiling—world knowledge, linguistic patterns, code structures, and cross-modal correlations are compressed into the weights. Yet, the model does not inherently know when to invoke specific capabilities, nor does it understand the required output formats, standards, or value orientations. The role of post-training is to awaken, shape, and align these latent capabilities to real-world tasks.

Consider an analogy: pre-training is akin to stocking a library with books, while post-training establishes the cataloging system, retrieval paths, and lending rules. The book collection (parameter scale) determines the library’s maximum capacity, but what users actually experience is the quality of retrieval and service (post-training proficiency).

Therefore, “achieving capability leaps through post-training rather than entirely new, larger-scale pre-training” has become the shared technical route for leading models. This approach offers at least three advantages:

• Cost Control: The computational overhead for post-training is typically much lower than a full pre-training run.
• Shorter Cycles: Models can iterate quarterly or even monthly, rapidly absorbing new data and feedback.
• Risk Mitigation: A failed experiment does not invalidate the entire base model; processes can be rolled back and ablated.

2. Post-Training Technology Stack: Six Key Components

2.1 Supervised Fine-Tuning (SFT): Defining “Good Answers” with High-Quality Trajectories

SFT is the first layer of post-training. It is not simply “retraining with Q&A pairs,” but rather a data-engineering-intensive route. The industry consensus is that data quality far outweighs quantity; tens of thousands of meticulously crafted high-quality samples often outperform millions of low-quality ones. Key practices include:

• Rejection Sampling: Generating multiple candidate answers for the same query using the base model, filtering the optimal trajectory via rules or a stronger discriminator, and feeding it back into training.
• Curriculum Design: Arranging task distributions from easy to hard—first solidifying basic formatting and instruction following, then gradually introducing long-horizon reasoning and complex tool calls.
• Coverage Alignment: Data distribution must cover real-world use cases, including multi-turn dialogue, repository-level code tasks, document understanding, and multimodal inputs, rather than just single-turn Q&A.

2.2 Preference Alignment: Solving “Correct but Suboptimal” Outputs

SFT can only mimic demonstrations; it cannot express “which answer is better.” Preference alignment injects paired preference signals into the model using methods like Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). This enables the model to choose the safer, more useful, and more instruction-compliant option among multiple “reasonable” candidates. This stage directly dictates the model’s “feel” and serves as the primary regulator for user experience metrics like hallucination and refusal rates.

2.3 Reinforcement Learning with Verifiable Rewards: Turning “Objective Correctness” into Training Signals

A major recent advancement in post-training is expanding RL from “subjective preferences” to “objective verifiability.” In tasks like coding, math, and formatted reasoning, answer correctness can be automatically judged by compilers, unit tests, or formal verification, providing exceptionally clean reward signals for RL:

def code_reward(submission: str, task: Task) -> float:
    """Verifiable reward example: construct reward based on hidden unit test pass rate"""
    if not compiles(submission):
        return 0.0
    passed = run_hidden_tests(submission, task.hidden_tests)
    reward = passed / len(task.hidden_tests)
    if has_hardcode_or_cheat(submission):   # Prevent reward hacking
        reward = 0.0
    return reward

These “result-verifiable” tasks are particularly suited for leveraging the reasoning potential of large parameter scales: through numerous “sample-verify-update” loops, the model gradually learns longer chains of thought and more rigorous step-by-step deduction. However, vigilance against reward hacking is essential—models might exploit loopholes in the reward function (e.g., hardcoding test answers). Therefore, robust reward function design, adversarial probing, and manual spot-checks are indispensable.

2.4 Agent and Tool Calling Training: From “Answering Questions” to “Getting Things Done”

In real productivity scenarios, a model’s value lies not in one-off answers, but in executing long-horizon, multi-step, tool-augmented tasks: reading and writing code repositories, operating terminals, invoking 3D modeling and CAD software, and driving game engines to build scenes. These capabilities cannot be learned from static corpora; they require building interactive training environments:

while not done and steps < max_steps:
    obs = env.render()
    action = model.decide(history, obs)   # Generate next step: command / tool call / code edit
    result = env.execute(action)          # Sandbox execution, returns real feedback
    history.append((obs, action, result))
    if task_passed(env):                  # Final validation
        done = True

Key supporting mechanisms include providing the model with sandboxed terminals, file systems, compilers, and professional tool interfaces; retaining both successful and failed trajectories for training; and performing step-level credit assignment to distinguish which operation was the critical contributor, passing the reward back to specific actions rather than just rewarding the final outcome.

2.5 Long Context Extension: The Engineering Implications of Million-Token Contexts

Extending the context window to million-token levels is not simply scaling up the positional encoding table; it requires an engineering suite: positional encoding extrapolation and interpolation strategies, long-text data ratio balancing with “needle in a haystack” information retrieval training, attention mechanism memory optimization, and KV cache management during inference. Long context capability directly determines whether a model can handle repository-level code understanding, lengthy contract reviews, and large project analysis. It is also the prerequisite for agents to “run far without getting lost.”

2.6 Native Multimodal Alignment

“Native visual understanding” implies that visual signals are unified with language during pre-training, rather than being patched on later. The post-training stage requires multimodal instruction alignment: chart Q&A, document layout understanding, UI operation, and video temporal reasoning. This allows the model to seamlessly switch between “seeing” and “reading,” directly converting structured screen information into actionable understanding.

3. Evaluation Systems: Proving “Potential is Unlocked”

Post-training efficacy must be validated through an evaluation loop, and evaluation design itself is becoming a core competitive advantage:

• Environment-Based Evaluation over Static Benchmarks: Giving a model a runnable code repository and a bug to fix in a real terminal to assess its multi-step operation and self-correction reflects engineering capability better than single-function completion.
• Vertical Deep-Dive Evaluation: Tasks like financial document analysis test joint understanding of cross-page tables, footnotes, and professional terminology, serving as a touchstone for long context and domain alignment.
• Professional Toolchain Evaluation: 3D modeling, CAD design, and game engine scenarios test a model’s ability to translate natural language requirements into precise tool operations, an advanced proving ground for Agent capabilities.
• Anti-Overfitting Mechanisms: Public benchmarks risk being “gamed.” Thus, maintaining hidden test sets, regularly updating questions, and supplementing with blind human evaluations are necessary to ensure score improvements stem from genuine capability rather than memorization.

4. Engineering Practices: The Full Pipeline from Training to Serving

4.1 Training Infrastructure

Post-training ultra-large models demands extremely high infrastructure requirements: hybrid parallelism strategies (data/tensor/pipeline/expert parallelism), memory optimization techniques like activation recomputation and optimizer state offloading, high-performance inference engine-supported rollout sampling, and a unified stack for training and inference frameworks (to avoid weight synchronization overhead between rollout and training). These capabilities typically rely on mature cloud compute and scheduling platforms. For instance, building training pipelines on cloud platforms that provide GPU compute, container scheduling, and monitoring services—such as Baidu AI Cloud—frees engineering teams from underlying operations, allowing them to focus on the algorithms themselves.

4.2 Inference Optimization and “Same-Capability High-Speed Versions”

Many teams simultaneously offer “Standard” and “High-Speed” service versions: aligned capabilities, but with output speeds differing by multiples. The underlying general technologies include: low-bit quantization (compressing weights and KV cache with controlled precision loss), speculative decoding (small model drafts, large model verifies), KV cache management and batch scheduling optimization, and more aggressive decoding parallel strategies. For developers, this means choosing a version based on latency sensitivity without compromising between capability and speed.

4.3 Open Weights and Private Deployment

Releasing complete model weights is becoming an industry trend. For enterprises and developers, open weights mean: local deployment to meet data compliance requirements, continued post-training based on proprietary domain data (domain SFT and preference alignment), and structural pruning and quantization optimization. Combined with general cloud capabilities like object storage, model serving, logging, and observability, a complete pipeline from weight acquisition and offline evaluation to online serving can be built at a relatively low cost.

4.4 Data and Feedback Loop

Post-training is not a one-time project but continuous operations: online bad case collection → manual annotation or automatic discrimination → integration into the next SFT/RL dataset → canary evaluation → full release. The operational speed of this loop largely dictates the model’s iteration speed and is more worthy of long-term investment than any single technical point.

5. Actionable Advice for Developers

• Evaluate First: Before selecting or building, validate models in environment-based evaluations closely matching your business, rather than solely relying on leaderboard scores.
• Data is King: Invest resources in building high-quality task trajectories and preference data; the return on investment is typically higher than simply piling up compute.
• Iterate Incrementally: Start with a minimal closed loop of SFT plus verifiable reward RL, gradually expanding to agent and multimodal training.
• Leverage Cloud Capabilities: Directly reuse cloud platforms for training compute, inference services, storage, and monitoring, focusing your energy on business differentiation.

6. Conclusion

When parameter scales cross the trillion-parameter threshold, the bottleneck to a model’s potential often lies not in “capacity,” but in “realization.” Post-training is the critical path transforming capacity into productivity. Using data as raw material, rewards as constraints, and environments as classrooms, it shapes a “knowledgeable but untrained” base model into a reliable collaborator capable of writing code, reading documents, operating tools, and completing long-horizon tasks. For the industry, this means the competitive focus shifts from “who has the largest pre-training” to “who has the most efficient post-training system.” For developers, understanding and leveraging this technology stack will be a fundamental skill for building AI applications in the coming years.