Unlocking Ultra-Large Model Capabilities Through Post-Training Without Retraining the Base

1. The Starting Point: Why “Not Retraining the Base” is a Necessity

When model parameter scales reach the trillion level, the compute power, data, and engineering cycles required for the pretraining phase grow exponentially. A complete pretraining run often requires thousands or even tens of thousands of accelerators running continuously for weeks to months. Furthermore, data cleaning, deduplication, ratio adjustments, and curriculum learning strategy tuning make the entire process highly unpredictable. Therefore, simply relying on “training a larger base model” to achieve capability improvements is no longer a viable path for most teams.

A more practical issue is that even if a larger-scale pretraining run is completed, the capability improvements may not align with the scenarios users actually care about. Tasks such as code generation, tool calling, long-document understanding, and visual reasoning often require more refined instruction alignment, preference learning, and behavioral constraints. These goals are precisely what post-training is designed to address.

This leads to a key conclusion: Pretraining determines what a model “knows,” while post-training determines what a model “can do” and how stably it can do it. For ultra-large models, shifting the optimization focus from pretraining to post-training is not just a cost-driven choice, but a sign of maturing capability engineering.

2. The Basic Logic of Post-Training Capability Release

Post-training is not a single action, but a pipeline consisting of multiple stages. It typically includes:

1. Supervised Fine-Tuning (SFT): Teaching the model to understand instruction formats, output specifications, and basic behaviors for domain-specific tasks.
2. Preference Alignment and Reinforcement Learning (RL): Using human feedback or rule-based rewards to make the model output more usable results on open-ended questions.
3. Specialized Capability Training: Concentrated optimization for specific directions like code, math, AI Agents, and multimodality.
4. Safety and Consistency Constraints: Reducing hallucinations, unauthorized actions, and format instability.

For ultra-large models, the core challenge of post-training is: How to enhance specific capabilities without destroying the existing knowledge breadth of the base model. If the training data ratio is improper or the learning rate is too aggressive, capability collapse can easily occur. For example, code capabilities might improve while general conversational quality degrades, or long-context performance becomes unstable. Therefore, post-training strategies must implicitly include the constraint of “capability preservation.”

3. Core Post-Training Practices: From Data Ratios to Multi-Stage Alignment

3.1 High-Quality Supervised Samples Over “More Samples”

For trillion-parameter models, SFT is extremely sensitive to data quality. Rather than injecting massive amounts of low-quality instruction pairs, it is better to build a moderately sized core dataset that covers key tasks and maintains high annotation consistency. In practice, this data can be organized across the following dimensions:

• Code Samples: Covering different granularities like requirement clarification, code generation, unit test completion, code review, bug localization, and refactoring suggestions.
• Agent Samples: Multi-turn tool selection, execution path planning, environment feedback processing, failure retries, and result verification.
• Long-Context Samples: Cross-chapter document Q&A, multi-file project understanding, long meeting minutes summarization, and regulatory clause comparative analysis.
• Visual Understanding Samples: Screenshot interpretation, UI automation, chart parsing, 3D scene description, and CAD drawing reading.

These samples should accurately reflect real-world task workflows rather than just providing static “input-output” pairs. Especially in Agent scenarios, the model needs to have seen the continuous process of “action → feedback → next action” to learn how to adjust its strategy amidst uncertainty.

3.2 Reinforcement Learning Enhances Task Completion Stability

SFT can make a model “know how to do” a task, but it struggles to guarantee that it will “get it right the first time and adjust when wrong.” This is where RL comes in. By defining quantifiable reward signals, RL allows the model to gradually converge to more stable and reproducible behaviors through multiple sampling and optimization rounds.

For code tasks, rewards can be derived from:

• Whether unit tests pass;
• Whether time and space complexity constraints are met;
• Whether the specified code style is followed;
• Whether the fix is completed with minimal changes;
• Whether explanations are provided for critical branches.

In Agent scenarios, rewards can be based on:

• Whether the correct tool was selected;
• Whether the goal was achieved within a limited number of steps;
• Whether errors in environment feedback were identified and proactively corrected;
• Whether irreversible, dangerous operations were avoided.

These rewards do not need to rely entirely on manual annotation; they can be automatically generated by rule engines, compiler feedback, and test environment responses, thereby supporting large-scale training. The key is that the reward granularity must be fine enough. You cannot only look at the final result, nor can you rely entirely on the intermediate process, otherwise the model will easily learn to “game the score” rather than genuinely complete the task.

3.3 Multi-Track Parallel Specialized Optimization

Ultra-large models typically do not mix all post-training objectives in the same stage. A common practice is to split them into multiple specialized tracks, preparing data, evaluation sets, and training experiments in parallel, and then verifying overall degradation through staged merging.

For example, this can be divided into four specialized tracks:

• Reasoning and Code Track: Focusing on logical reasoning, function-level generation, and multi-file context modifications.
• Agent and Tool Calling Track: Focusing on multi-turn interaction, planning, retries, and tool selection.
• Long-Context and Document Track: Focusing on cross-paragraph localization, information extraction, and conditional comparison.
• Vision and Multimodal Track: Focusing on screenshots, charts, model diagrams, and 3D scenes.

After each specialized track is trained, regression testing must be performed using a general evaluation set. If a significant drop in general capabilities is observed, the data ratio needs to be adjusted, or “general capability preservation replay samples” must be added. This process of “separate training — merged regression — rebalancing” is a standard form of engineering for ultra-large model post-training.

4. Designing an Evaluation System to Reflect True Capability Changes

Evaluation during the post-training phase cannot rely solely on leaderboard scores. Public benchmark scores are easily indirectly fitted during the training process, causing them to detach from real-world business performance. A more robust evaluation system should consist of three types of metrics:

4.1 Capability Benchmarks

Used to measure whether the model has reached a deliverable level in specific domains. For code scenarios, this can include:

• The completion rate of real-world terminal programming tasks;
• The success rate of cross-file modifications within a given repository structure;
• The test pass rate for complex function generation;
• The final fix rate in multi-turn debugging tasks.

These benchmarks should utilize hidden test sets and executable feedback wherever possible to prevent the model from scoring via superficial pattern matching.

4.2 Behavioral Stability Evaluation

Used to measure whether the model performs consistently across multiple similar inputs. This can be designed from the following dimensions:

• The variance in success rate when repeating the same task 100 times;
• Robustness to minor variations in instruction phrasing;
• Attention stability within ultra-long contexts;
• Whether the model starts correctly but gradually drifts off-topic.

Stability is often more important than a single high score, especially for models embedded in production pipelines.

4.3 Human Review of Task Completion Quality

Domain experts conduct structured reviews of model outputs, focusing on:

• Code maintainability;
• Accuracy of solution explanations;
• Whether tool calls are necessary and reasonable;
• Completeness of error handling;
• Whether outputs “appear reasonable but are practically ineffective.”

Although human review is costly, it can uncover issues that automated metrics miss. In practice, a combination of “automated initial screening + manual deep-dive spot checks” is typically employed.

5. Engineering Implementation: Training, Inference, and Deployment Synergy

Post-training is not just an algorithmic issue; it is a systems problem. For ultra-large models, the following engineering factors directly impact the final outcome:

5.1 Training Efficiency and Memory Management

Post-training typically requires higher memory usage than inference. To control costs, the following techniques can be combined:

• Using gradient checkpointing to reduce activation memory storage;
• Sharding optimizer states to reduce per-GPU memory pressure;
• Applying sequence parallelism for ultra-long samples;
• Adopting different parallelism strategies for different stages;
• Appropriately reducing optimizer state precision without affecting convergence.

These methods do not alter the model structure but can significantly increase training throughput, allowing teams to complete more experiment iterations in less time.

5.2 Parallel Implementation for High-Speed Inference

The same model can derive different deployment forms: a full version for scenarios extremely sensitive to performance, and a high-speed version for large-scale online invocations, interactive programming tools, or real-time Agent scenarios. While maintaining the same weights, the high-speed version typically reduces latency through:

• Optimizing KV cache management within the inference engine;
• Employing more aggressive dynamic batching strategies;
• Segmenting long sequences and advancing them via a pipeline;
• Specific optimizations for autoregressive decoding on particular hardware configurations.

Additionally, acceleration can be achieved through distillation or pruning redundant intermediate computations, though care must be taken not to significantly degrade capabilities. For ultra-large models, system-level optimizations are generally prioritized over altering the model architecture.

5.3 Open Weights and Private Deployment Requirements

Some teams need to deploy models within internal environments to ensure data compliance, network isolation, or cost control. In these cases, open-weight models provide greater flexibility. However, open weights do not mean “plug-and-play.” Actual deployment still requires:

• Model conversion and inference engine adaptation;
• Memory and latency testing for long-context scenarios;
• Secure integration of tool calling interfaces with execution sandboxes;
• Implementing permissions, auditing, logging, and alerting mechanisms;
• Establishing model update and version rollback strategies.

These tasks often consume more team manpower than the model training itself, so deployment feasibility and resource assessments should be established before introducing a model.

6. A Reference Post-Training Iterative Closed Loop

In real-world projects, post-training for ultra-large models is rarely a one-time process; it advances iteratively. A viable closed loop includes the following steps:

1. Task Baseline Analysis: Sampling real tasks from production to evaluate where the current model fails.
2. Failure Mode Clustering: Categorizing errors into understanding, planning, generation, tool usage, formatting, etc.
3. Targeted Data Construction: Generating supplementary samples for high-frequency failure categories and performing quality filtering.
4. Small-Scale Experiments: Rapidly validating training gains on smaller parameter versions or data subsets.
5. Full-Scale Post-Training: Applying validated strategies to the large model, incorporating general capability preservation samples.
6. Regression Evaluation: Running both specialized benchmarks and general regression sets concurrently to confirm no significant capability degradation.
7. Gradual Deployment: Rolling out to low-risk scenarios first to observe real user feedback.
8. Feedback Sample Updating: Feeding post-deployment failure cases back into the next round of training data.

This closed loop is essentially an operational model for “continuously unlocking model potential through post-training.” It does not aim to solve all problems in a single training run but allows the model to gradually approximate business objectives through real-world feedback.

7. Conclusion: The Era of Post-Training Capability Engineering

When base model pretraining is no longer iterated frequently, a team’s core competitiveness gradually shifts towards post-training capability engineering. This includes the organization of training data, the design of reward signals, the splitting and merging of specialized capabilities, the construction of evaluation systems, and the collaborative optimization of training, inference, and deployment.

The true differentiator will no longer be “who has the larger model,” but rather:

• Who understands better where the model fails on specific tasks;
• Who can build high-quality correction data faster;
• Who can design finer-grained reward and regression mechanisms;
• Who can connect the training iteration closed loop with production feedback.

For enterprises, this means shifting from a “buying models” mindset to a “continuously improving models” mindset. Successfully navigating this transformation—while maintaining data compliance, cost control, and deployment stability—will deliver far more practical value than simply focusing on parameter scale.