Data Reuse and Transfer Learning in Large Model Training

1. Background: Large Model Training Has Entered the “Data Engineering” Era

In the development of large language models, model architecture, compute scale, and training data form three core pillars. As model structures gradually converge and compute scheduling solutions mature, the acquisition, cleaning, augmentation, and reuse of training data are becoming the key factors that determine the upper bound of model capability.

Early large model training relied primarily on massive static text from public web pages, books, and code repositories. The larger the data scale and the broader the domain coverage, the stronger the model’s foundational language ability. However, as models move into scenarios such as dialogue, reasoning, and tool use, relying solely on static text is no longer sufficient. Models need to learn more complex reasoning processes, more consistent instruction-following capabilities, and more stable output formats.

As a result, later training stages began introducing more targeted data construction methods, including synthetic data generation, reasoning trajectory collection, cross-model data distillation, curriculum learning, and staged training. The goal of these methods is no longer simply to expand data volume, but to significantly improve the information density and task relevance of the data.

This article focuses on data reuse and transfer learning practices in the post-training stage of large models, with emphasis on three common strategies: training with synthetic data, distillation training with reasoning trajectories, and experience transfer based on architecture design. It also explains how these strategies are implemented in real training pipelines, drawing on common engineering practices.

2. Synthetic Data: From Data Scarcity to Data Augmentation

2.1 Why Synthetic Data Is Needed

High-quality human-annotated data is costly, slow to produce, and limited in volume. In some complex reasoning tasks, annotators themselves may not be able to consistently provide correct answers. Synthetic data, generated automatically by existing models, can significantly lower the barrier to data acquisition and rapidly expand data diversity.

For example, in tasks such as mathematical reasoning, code generation, and logical judgment, a more capable model can first generate a large number of samples with reasoning processes, which are then used to train a less capable model. This approach not only expands the data scale but also allows the weaker model to learn richer intermediate reasoning paths.

Synthetic data is not equivalent to “generating text with a model.” A rigorous synthetic data pipeline typically includes the following steps:

1. Task design: Define the input-output format, difficulty distribution, and boundary conditions of the target task;
2. Seed data preparation: Collect a small number of high-quality samples to guide the generation direction;
3. Batch generation: Have the generation model produce candidate samples in bulk according to task templates;
4. Quality filtering: Remove samples with format errors, factual errors, or logical breaks through rules, classifiers, or manual spot checks;
5. Diversity control: Avoid excessive uniformity in expression and reasoning patterns within the same batch of synthetic data;
6. Deduplication and decontamination: Prevent overlap with evaluation sets and validation sets, which would undermine evaluation credibility.

2.2 Typical Application Scenarios for Synthetic Data

In real engineering practice, synthetic data is most commonly used in the following scenarios:

• Instruction fine-tuning data augmentation: Generate a large number of instructions with different styles but similar semantics based on a small set of human-written instruction samples;
• Reasoning process completion: Generate step-by-step reasoning processes based on existing problems or questions to form complete “question-reasoning-answer” training samples;
• Multilingual alignment: Translate or rewrite high-quality training data from one language into other languages to improve multilingual capability;
• Safety and compliance sample construction: Construct adversarial and boundary-case inputs to train models to refuse dangerous requests or identify anomalous behavior;
• Long-tail task coverage: Generate more samples in a targeted manner for low-frequency but important task types to improve model performance in long-tail scenarios.

In these scenarios, synthetic data is not a simple replacement for human-annotated data but an important supplement. A mature training pipeline typically mixes human-annotated data, cleaned natural data, and synthetic data at a certain ratio.

2.3 Quality Risks of Synthetic Data

Although synthetic data can rapidly expand scale, its quality depends heavily on the capability of the generation model itself. If the generation model has systematic biases in a certain domain, the training data it generates will amplify those biases, ultimately causing the trained model to inherit or even reinforce these flaws.

Common risks include:

• Factual drift: Generated samples contain plausible but factually incorrect descriptions;
• Reasoning path homogenization: A large number of samples adopt similar reasoning templates, weakening model generalization;
• Style homogenization: Generated data converges in tone, sentence structure, and logical organization, leading to monotonous model output;
• Annotation noise accumulation: Automatically generated labels or scores may contain errors, and the noise is absorbed by the model during training;
• Data contamination: Generated samples inadvertently overlap with public evaluation sets, leading to inflated evaluation results.

Therefore, the introduction of synthetic data requires a rigorous quality control mechanism. Typically, one or more rounds of filtering are added after generation, including rule-based regex checks, model-based automatic scoring, sampled manual review, and similarity comparison with existing datasets. Only when quality control is in place can synthetic data become a stable input in the training pipeline.

3. Reasoning Trajectory Distillation: Teaching Models to “Think”

3.1 What Are Reasoning Trajectories

In natural language processing tasks, reasoning trajectories refer to the intermediate reasoning content produced by a model between receiving an input question and outputting a final answer. This content may include step-by-step calculations, logical deductions, hypothesis verification, self-correction, and other elements.

For example, for a math word problem, the reasoning trajectory might be:

Problem: A store purchased a batch of goods at a cost of 80 yuan per item and priced them at a 20% profit margin. During the actual sale, a 10% discount was applied. Find the actual profit per item.
Step 1: Calculate the list price. List price = 80 × (1 + 20%) = 96 yuan.
Step 2: Calculate the actual selling price. Actual selling price = 96 × 0.9 = 86.4 yuan.
Step 3: Calculate the actual profit. Actual profit = 86.4 - 80 = 6.4 yuan.
Final answer: The actual profit per item is 6.4 yuan.

These intermediate steps show how the model organizes its thinking, calls upon knowledge, and verifies feasibility. For model training, these intermediate processes carry extremely high information value because they reveal the reasoning structure behind the answer, not just the final output.

3.2 Learning from Reasoning Trajectories

During the post-training stage, a batch of high-quality reasoning trajectories generated by a stronger model can be collected and fed as training data to the model being trained. This approach is commonly called “reasoning trajectory distillation” or “process distillation.”

Compared with traditional training that only learns input-output pairs, training with reasoning trajectories enables the model to learn:

• How reasoning unfolds: How to decompose a large problem into manageable sub-problems when facing complex tasks;
• How to express intermediate conclusions: How to clearly describe each deduction step in natural language;
• Error identification and correction strategies: How to backtrack and adjust when contradictions are detected during reasoning;
• How conclusions connect to the process: How to naturally transition from intermediate reasoning to the final answer.

In practice, reasoning trajectory distillation typically follows this pipeline:

1. Task selection: Select tasks suitable for demonstrating reasoning processes, such as math, logic, code debugging, and complex question answering;
2. Trajectory generation: Have a stronger model generate outputs containing complete reasoning processes on these tasks;
3. Quality screening: Retain only trajectories with correct reasoning processes, reasonable conclusions, and logical coherence;
4. Format alignment: Unify trajectories from different sources into the format used during training;
5. Mixed training: Mix reasoning trajectory data with regular instruction data at a certain ratio to prevent the model from over-relying on lengthy reasoning for simple tasks;
6. Effect evaluation: Evaluate the balance between accuracy, reasoning completeness, and output conciseness after introducing reasoning trajectories on the validation set.

3.3 Considerations for Reasoning Trajectory Distillation

Although reasoning trajectory distillation can significantly improve performance on complex tasks, there are several easily overlooked issues in implementation.

First, trajectory length affects reasoning efficiency. If a large number of training samples contain lengthy reasoning processes, the model may learn to elaborate detailed reasoning on every task, resulting in slow responses and redundant output for simple questions. The solution is to include both “quick answer” and “detailed reasoning” samples in the training data and let the model learn to choose the appropriate granularity based on task complexity.

Second, incorrect trajectories are not always harmful. Some annotated “error-correction” trajectories can help the model learn how to recover from mistakes. However, if unfiltered incorrect trajectories are mixed in large quantities, the model may instead learn incorrect reasoning patterns. Therefore, the use of incorrect trajectories requires a more fine-grained annotation strategy.

Third, reasoning trajectories may contain factual knowledge. If this knowledge conflicts with existing knowledge in the model’s parameters, it may cause confusion in the model’s internal knowledge during training. It is recommended to perform additional verification of factual statements in reasoning trajectory data to reduce conflicts between training signals.

Fourth, the diversity of distillation sources needs to be evaluated. Reasoning trajectories generated by different source models differ in style, depth, and symbol usage. If trajectories with large differences are directly mixed, training stability may decrease. It is recommended to unify formats during data preprocessing and to record and monitor source distributions.

4. Architecture Reference and Model Design Transfer

4.1 The Basic Logic of Architecture Transfer

In large model development, the transfer and reference of architecture designs is a common industry practice. After a particular architecture demonstrates strong performance at a specific scale or on specific tasks, other teams reproduce its key designs based on public information and adapt them to their own needs.

Common architecture design elements include:

• The ratio between the number of network layers and hidden dimensions;
• The specific implementation of attention mechanisms;
• The activation function and expansion ratio of feed-forward networks;
• The type and parameterization of positional encodings;
• The insertion position and parameter initialization strategy of normalization layers;
• The design of caching mechanisms during inference.

Architecture transfer does not mean “copying verbatim.” In actual training, most teams adjust existing architecture ideas based on their own data scale, training budget, and deployment scenarios. For example, if the target scenario is sensitive to inference latency, they may reduce the number of layers and increase width; if the target scenario requires strong long-text processing, they may adjust positional encodings or attention window strategies.

4.2 Synergy Between Architecture Transfer and Data Training

Architecture design does not exist in isolation; it jointly determines the model’s final performance along with training data and training strategies. The same architecture design can produce very different results under different data distributions and training hyperparameters.

Therefore, when referencing an architecture design, the following questions typically need to be evaluated simultaneously:

• To what extent does the architecture depend on a specific data distribution?
• What are the architecture’s requirements for training stability?
• Does the architecture’s resource consumption during inference meet deployment targets?
• Is the architecture convenient for later fine-tuning and continuous iteration?

For example, some architectures perform stably in very-large-scale training, but when transferred to medium scale, learning rate schedules, gradient clipping thresholds, or weight decay coefficients may need adjustment. Directly applying the original configuration without adaptation may fail to reproduce the expected results.

4.3 Controllability in Experience Transfer

In actual development workflows, architecture referencing is typically “selective absorption” rather than wholesale copying. Teams decide which designs to introduce and which to abandon based on existing infrastructure, historical training experience, and assessment of the target scenario.

This selection process itself is part of engineering capability. It is recommended that teams introduce new architecture designs by gradually validating each design’s contribution to final performance through small-scale ablation experiments, rather than introducing multiple changes at once, which makes attribution difficult.

5. Engineering Implementation of Data Reuse Strategies

5.1 Data Version Management

In post-training of large models, training data undergoes multiple version iterations. Each adjustment to data cleaning rules, change in synthetic data generation logic, or update to reasoning trajectory sources can result in substantive changes to the training data version.

It is recommended to establish clear version identifiers for each version of training data and record the following metadata:

• Data sources and their proportions;
• Cleaning rules and filtering conditions;
• Synthetic data generation parameters;
• Deduplication strategies and thresholds;
• Data scale and distribution statistics;
• Overlap check results with validation and evaluation sets.

The benefit is that when model performance becomes anomalous, you can quickly trace back to the specific data version, identify the root cause, and avoid blindly searching through a massive dataset.

5.2 Data Mixing and Curriculum Learning

In actual training, different types of data are typically not mixed in equal proportions. Common strategies include:

• Fixed-ratio mixing: Mix instruction data, reasoning trajectory data, dialogue data, code data, etc., at preset ratios;
• Curriculum learning adjustments: Use simpler samples in early training and gradually introduce more complex samples later;
• Dynamic sampling strategies: Dynamically adjust the sampling probability of each data type based on model performance on the validation set;
• Staged training: First complete foundational training on general data, then perform targeted reinforcement on domain-specific data.

The choice of strategy depends on the training objective. If the goal is to improve the model’s overall capability, a combination of fixed-ratio mixing and dynamic sampling is typically used. If the goal is to rapidly improve performance on a specific task, staged training may be used, with domain-specific data concentrated in the later stage.

5.3 Compliance Considerations in Data Reuse

When using synthetic data, reasoning trajectories, or data generated from other models for training, it is necessary to pay attention to the compliance boundaries of data usage.

General principles include:

• Clearly identify data sources and the scope of their usage licenses;
• De-identify data involving personal or sensitive information;
• Avoid mixing evaluation set or benchmark data into training data;
• Filter out biases and harmful content that may be present in synthetic data;
• Record data flow paths to ensure auditability.

In practice, it is recommended to complete compliance review before data enters the training pipeline, rather than performing checks after training is complete. Once training data has entered model parameters, completely removing its influence typically requires additional training or retraining, which is costly.

6. Practical Recommendations and Summary

6.1 Key Practical Recommendations

Based on the analysis above, the following practical recommendations can be referenced when introducing data reuse and transfer learning in post-training of large models:

First, establish clear data documentation. Each batch of training data should be accompanied by complete records of generation, cleaning, filtering, and deduplication for future traceability.

Second, validate at small scale before large-scale application. Any new synthetic data strategy or reasoning trajectory distillation approach should first be validated on a smaller data scale to confirm a positive impact on model metrics before expanding to full-scale training.

Third, focus on data diversity. Synthetic data and reasoning trajectories tend to converge in style. During training, data distribution should be continuously monitored, and diversification strategies should be introduced when necessary to avoid homogenized model output.

Fourth, balance reasoning depth with response efficiency. Learning from reasoning trajectories should help the model be more accurate on complex tasks, not make all responses lengthy. The data should provide the model with examples of answers at different levels of granularity.

Fifth, value the independence of the evaluation system. Training data and evaluation data must be strictly separated. Especially when training data contains large amounts of automatically generated content, it is important to prevent hidden associations between the generation source and the evaluation set.

6.2 Summary

Post-training of large models has shifted from simply “increasing data volume” to “improving data quality and information density.” Methods such as synthetic data generation, reasoning trajectory distillation, and architecture design transfer are all aimed at more efficiently improving model task performance under limited compute and data budgets.

The common thread among these methods is that they all rely on reusing existing model capabilities or existing training experience. Whether generating training samples with a stronger model or referencing design elements from mature architectures, the essence is to accelerate the training of new models by transferring existing knowledge.

In practice, whether data reuse and transfer learning can produce positive results depends on whether quality control is rigorous, whether data version management is thorough, whether the evaluation system is independent, and whether the training strategy matches the target scenario. The accumulation of these engineering capabilities often has more long-term value than a single improvement in model metrics.