Post-Training Paradigms for Ultra-Large Models: Unlocking the Real Potential of Trillions of Parameters

1. Why Capability Unlocking for Ultra-Large Models Is Shifting Toward Post-Training

For years, industry discussion around large models centered on the pre-training phase: more parameters, richer corpora, and longer training times seemed to form a direct path to capability gains. But as model scale reaches the trillions-of-parameters level, the marginal returns from simply scaling pre-training costs are diminishing. The reason is straightforward—pre-training primarily addresses “world knowledge compression” and “language modeling capability,” yet real tasks often require models to continuously trial-and-error in specific environments, understand feedback, and adjust behavior. These capabilities are hard to obtain from unsupervised corpora alone.

As a result, the importance of post-training is being reassessed. Post-training typically includes supervised fine-tuning, preference alignment, reinforcement learning, and tool-use training. It is no longer just about making models “more obedient”—it is about enabling models to exhibit stable, reusable, and verifiable capabilities on concrete tasks. Especially for code generation, agent operations, professional document analysis, and 3D scene construction, models must be able to interact with external environments: execute commands, read returned information, and correct strategies based on errors. This capability cannot be directly conferred by pre-training corpora; it must be gradually formed through extensive real or simulated trial-and-error.

From a cost-benefit perspective, substantially increasing post-training compute on top of a mature base model is often more cost-effective than pre-training a larger model from scratch. The base model already possesses sufficient language understanding and common-sense reasoning foundations, so post-training compute can be more concentrated on reinforcing task-relevant capabilities. This also means the model iteration cycle can be shortened from “months to a year” to “weeks,” while keeping capability improvements observable.

2. How Post-Training Amplifies a Base Model’s Task Capabilities

The core mechanisms through which post-training unlocks potential can be summarized in three points: task distribution expansion, process feedback utilization, and policy stability improvement.

1. Task Distribution Expansion: From General Alignment to Domain Coverage

Traditional instruction-tuning data tends to lean toward everyday question answering, summarization, and rewriting, with limited coverage. To make models competent in professional domains, the training task distribution must be expanded to more granular scenarios. In coding tasks, for example, models must not only generate a single code snippet but also handle repository-level code modifications, debugging after test failures, cross-file dependency analysis, and interface invocation with version compatibility issues. In financial document analysis, models must handle structured and unstructured mixed information such as tables, footnotes, audit opinions, and financial metric relationships.

The key to expanding task distribution is not simply increasing data volume, but constructing samples with “process complexity.” A task should contain multiple elements—goals, constraints, intermediate feedback, and expected results—rather than a simple input-output pair. Only then can the model learn the complete reasoning chain from goal to result during post-training.

2. Process Feedback Utilization: Reinforcement Learning Enters the Real Task Loop

Reinforcement learning in post-training is no longer limited to static feedback such as “human preference ranking”; it increasingly uses “executable feedback.” In code tasks, for example, after generating a patch, the model can actually run test cases and receive rewards based on pass rates, error types, and performance regressions. Such feedback is explicit, repeatable, and automatable—well suited for large-scale reinforcement learning training.

Under this paradigm, the model’s improvement logic changes: it is no longer trained merely to “write more like the correct answer” but to “perform better in the environment.” For instance, when facing code that triggers edge cases, the model must diagnose the cause after execution failure, attempt modifications, and re-execute until constraints are satisfied. This “generate—execute—observe—adjust” loop gives the model debugging experience akin to that of a human engineer.

Notably, this training approach places higher demands on infrastructure. Each training sample may require an independent sandbox environment, resource limits, timeout control, and log collection. If this infrastructure is insufficiently stable, training signals become contaminated with noise, degrading policy quality.

3. Policy Stability: From Single Correctness to Long-Term Reliability

A single correct answer does not equal a reliable policy. One goal of post-training is to keep model performance stable across multiple similar tasks and adaptive when facing variation. For example, different modules of the same codebase may have different styles and dependencies, and the model must still output workable solutions under these differences.

Policy optimization methods in reinforcement learning, such as proximal policy optimization, can improve training stability by limiting the magnitude of policy updates. More importantly, however, the training tasks themselves must cover sufficiently diverse initial states and constraints. Otherwise, the model easily overfits to a certain error pattern or document structure and generalizes poorly in real use.

3. Key Task Scenarios and Post-Training Design

1. Code Generation and Terminal Programming

Terminal programming tasks require models not only to generate code snippets but also to locate problems in a complete project environment, modify files, run commands, and interpret results. Standard evaluations for such tasks typically focus on “completion rate” and “first-pass rate.” In actual training, however, what deserves more attention is the model’s repair capability: after a first attempt fails, can the model make effective corrections based on error information?

Below is a schematic of a typical training sample structure:

{
  "repo": "internal://project-x",
  "issue": "Fix duplicate notifications caused by payment callback timeouts",
  "constraints": ["Keep existing API signatures unchanged", "Add unit tests"],
  "environment": {
    "language": "python",
    "test_command": "pytest -q",
    "timeout_sec": 120
  },
  "feedback_loop": true,
  "max_attempts": 3
}

In this setup, the model must not only generate a modification plan but also execute tests in the environment, read failure logs, and use them to inform the next attempt. The multi-round feedback mechanism significantly changes the model’s policy—it begins to learn to “read test output first, then make targeted modifications” rather than blindly replacing code.

2. Agents and Long-Horizon Tasks

Agent-type tasks typically involve multi-step operations, state maintenance, and subgoal decomposition. The model must record key information during execution, judge current progress, switch tools, and backtrack when necessary. The post-training focus for such tasks is “planning capability” and “tool selection capability.”

For example, a typical task might be: extract specified fields from several local files, perform data cleaning, generate a brief statistical report, and write the results to a specified directory. The model must invoke file-reading tools, data-processing tools, and document-generation tools, and handle fault tolerance when files are missing or formats are abnormal.

Training samples need to explicitly annotate the reasonable state of each step, or construct rewards from intermediate results returned by the environment. For instance, whether a file exists, whether fields are correctly extracted, and whether the output format conforms to specifications can all serve as computable feedback signals. In this way, the model gradually learns to perform necessary pre-checks before invoking tools rather than directly assuming the environment state.

3. Professional Document Analysis and Structured Reasoning

Professional document analysis—such as financial reports, audit documents, and technical white papers—requires models to have cross-paragraph and cross-table relational understanding, and to handle implicit assumptions and conditional constraints. The difficulty of such tasks is that correctness often cannot be judged by simple rules, so semi-automated verification mechanisms must be constructed.

One scalable approach is “programmatic validation”: have the model output structured conclusions with supporting evidence, then use scripts to check whether the conclusions match key numbers, dates, and entity names in the source text. If they do not match, give a negative reward and return the location of the discrepancy. This training approach can significantly reduce the model’s error rate in numerical citations and clause correspondence.

In addition, long-context reasoning capability is particularly critical in this scenario. The model needs to build an index-like understanding across the entire document rather than relying only on the most recent passage. Post-training can specifically strengthen the model’s long-distance information linking capability by constructing tasks such as cross-page question answering, contradiction detection, and conditional clause judgment.

4. 3D Modeling and Tool Invocation

3D modeling tasks are unfamiliar to most people, but they are a typical scenario that embodies tool invocation and spatial reasoning capabilities. The model must control modeling tools through scripting interfaces, generate geometry, set materials, adjust camera viewpoints, and output renderable scene files based on natural language descriptions.

The feedback signals for such training tasks are relatively clear: whether the script executes successfully, whether generated objects exist, whether geometric parameters are within reasonable ranges, and whether the rendering result contains expected elements. The model must learn the closed loop of “natural language instruction—script invocation—execution feedback—parameter adjustment.” Because tool invocation typically has long latency and many failure modes, post-training must pay special attention to the model’s patience and error-correction ability, preventing it from giving up too early after a first failure.

4. Training Engineering and Infrastructure Optimization

Post-training effectiveness depends not only on algorithms but also heavily on engineering systems. Compared with pre-training, post-training is more sensitive to environment real-time performance, feedback quality, resource isolation, and scheduling efficiency.

1. High-Throughput Task Execution Sandboxes

In code and agent training, each sample may require an independent execution environment. Sandboxes need fast startup, resource limits, network isolation, log collection, and exception cleanup. A common design is to use containerized execution units, allocating an independent container to each task and reclaiming it immediately after completion. To improve throughput, container templates can be reused within a single machine, and partial state can be retained after execution for the next attempt.

2. Standardization and Denoising of Feedback Signals

Environment feedback comes in diverse forms, potentially including stack traces, timeout prompts, assertion failures, and log streams. If these raw outputs are fed directly to the model as input, they may contain substantial noise. Therefore, training pipelines usually need to standardize feedback: extract key segments, remove timestamps and random paths, unify error classification, and attach retry suggestions. This reduces context length pressure while making reward modeling more stable.

3. Engineering Adaptation for Long Contexts

Large context windows are a double-edged sword in post-training. On one hand, multi-round interactions and long-document tasks require longer contexts; on the other hand, long contexts significantly increase memory usage and communication overhead. In practice, training typically adopts a hierarchical context management strategy: keep recent interactions as full sequences, compress earlier content into summaries or retrieval key-values, maintaining necessary information while controlling training costs.

4. Adaptive Task Scheduling

Different task types vary greatly in duration: a simple code run may take only seconds, while a validation involving a full test suite may take minutes. If the scheduling policy is unreasonable, slow tasks become bottlenecks in the training cluster. Therefore, tasks should be dynamically grouped based on historical average duration and resource usage, with long tasks assigned to dedicated queues and short tasks routed through a fast lane to avoid mutual blocking.

5. Evaluation Design and Capability Verification

Whether post-training is effective must be verified through evaluations that closely reflect real usage scenarios. Traditional benchmarks often measure only static question-answering accuracy and struggle to reflect performance on process-oriented tasks. Therefore, post-training evaluation systems need to introduce process metrics and environment execution metrics.

1. Real Task Completion Rate

In terminal programming tasks, for example, evaluation metrics can include first-attempt pass rate, completion rate within a limited number of attempts, average number of attempts, and post-repair pass rate. Compared with checking only final results, these metrics better reflect the model’s stability in real engineering environments.

2. Tool Invocation Accuracy and Fault Tolerance

Evaluate whether the model selects the correct tool, sets parameters reasonably, and makes meaningful adjustments after failures, rather than merely checking whether the final output meets expectations. Such evaluation requires recording the complete interaction trajectory and analyzing the reasonableness of model behavior.

3. Long-Context Information Retention

By constructing tasks that require cross-distance information association—such as finding contradictions across hundreds of thousands of tokens or verifying totals across multi-page tables—evaluate whether the model suffers from “forgetting intermediate information” or “focusing only on local content.”

6. Future Evolution of the Post-Training Paradigm

Post-training is moving from “auxiliary tuning” to “the main battlefield for task capability.” It is foreseeable that future iterations of ultra-large models will increasingly adopt a “fixed base + domain-specific post-training” model. This reuses the knowledge foundation of large-scale pre-training while rapidly producing usable versions for different industry needs.

At the engineering level, post-training infrastructure will further evolve toward automation and standardization. Task environment creation, feedback signal collection, reward model updates, and training-evaluation linkage will form a closed-loop iterative system. Models will no longer be one-time release products but capability services that can be updated weekly or even daily.

For developers, this means the way large models are used will also change: focus not only on the model’s parameter scale but also on whether it has undergone sufficiently thorough reinforcement learning training in key task environments and whether it has good tool invocation and self-correction capabilities. Ultimately, a model’s value lies not in how large it appears but in how many real-world problems it can solve. Post-training is precisely the key engineering path that converts parameter count into usability.