LLM Alignment Training: Technical Routes for Human and AI Feedback

1. Reframing the Question from “Which Model Is Stronger” to “Where Does Capability Come From”

Comparisons between two large models often change with the evaluation set, version number, and testing time. A model that performs well on coding tasks may lag on long-context reasoning. A more useful question is therefore: which stages produce the differences in model capability?

In general, LLM capability is determined by three layers: the scale of data and compute in pretraining, instruction tuning and preference alignment in post-training, and engineering optimization and context management during inference. These layers interact, and a change in any one of them can alter final performance.

2. Pretraining: The Foundation of Capability

During pretraining, a model learns language structure and world knowledge from large-scale text. Data engineering typically includes the following steps:

1. Source filtering: divide data sources by domain, language, and quality.
2. Cleaning and deduplication: remove garbled text, repeated passages, and low-quality pages to reduce the risk of the model memorizing duplicate content.
3. Mixture design: adjust the proportions of code, math, dialogue, encyclopedia, and other data. This directly affects model capability on corresponding tasks.
4. Compliance review: confirm the authorization status of data. Copyright and personal information protection are common industry concerns. Acquisition channels should comply with local regulations, and traceable records should be retained.

Synthetic data is a common supplementary approach in recent years. Model-generated samples can expand training volume for long-tail tasks, but if the generation process lacks diversity control, errors and biases can be amplified over multiple iterations. In practice, sampled human review is usually needed, along with deduplication and quality scoring of generated samples.

3. Post-Training: From Instruction Following to Preference Alignment

Post-training generally consists of three steps.

The first step is supervised fine-tuning. Using paired instruction-response data, the model learns to answer in the required instruction format. Data quality is more important than quantity; repetitive or templated samples can make model outputs monotonous.

The second step is preference modeling. Annotators rank multiple answers to the same question, and a reward model is trained to score answers. A commonly used pairwise loss function is shown below:

# Pairwise training objective for a reward model (pseudocode)
def pairwise_loss(score_preferred, score_rejected):
    # Objective: the preferred answer should score higher than the rejected answer
    margin = score_preferred - score_rejected
    return -log(sigmoid(margin))

The third step is policy optimization. The model is updated through reinforcement learning using reward model scores as signals. To prevent the model from drifting away from the original language distribution, training usually includes a KL divergence constraint that limits the gap between the new policy and the reference policy.

4. Comparing Two Feedback Sources

Preference data can come from humans or from AI-generated judgments based on explicit rules. The two differ across several dimensions:

Dimension	Human feedback	AI-based feedback
Cost	High annotation labor cost and long cycles	Low marginal cost and scalable generation
Consistency	Large variation in judgments among annotators	Uniform rules, but affected by rule design
Bias source	Annotators’ cultural background, language habits, and scoring standards	Coverage and wording of rule clauses
Auditability	Relies on annotation guidelines and sampling records	Traceable to specific rule clauses

A typical risk of human feedback is preference transmission. If the annotator group or scoring standard favors a certain style of expression, the reward model learns that preference. After policy optimization, that style may appear frequently in model outputs, creating a recognizable “style fingerprint.” This shows that the annotation process itself is part of model behavior and needs to be included in system design.

Principle-based AI feedback writes requirements such as safety and helpfulness into explicit clauses, and a model judges which of two answers is better according to those clauses. Its advantages are that the rules are readable and modifiable; its disadvantages are that cases outside the rules may be overlooked, and biases in the judging model can be carried into training. In practice, the two approaches are often combined: humans handle rule development and sampling review, while AI handles large-scale initial screening.

5. How to Evaluate Model Capability Fairly

Conclusions such as “leading” need to be based on reproducible evaluation. Common evaluation pitfalls include:

• Overlap between evaluation sets and training data, which inflates scores;
• Prompt wording changes that clearly affect results;
• Comparing only a single run and ignoring randomness;
• Reporting only average scores and masking differences between tasks.

A more robust approach is to build separate evaluation sets by task type (code, dialogue, math, long text), use blind review so that reviewers do not know the source of answers, run each task multiple times, and report confidence intervals. An illustrative workflow is shown below:

# Evaluation workflow (illustrative)
for task in eval_tasks:
    for model in candidate_models:
        for run in range(num_runs):
            response = model.generate(task.prompt)
            record(task.id, model.id, run, score(response))
summary = compute mean and confidence interval by task

Reviewer consistency also needs to be quantified. Two reviewers can score independently, and consistency metrics such as Cohen’s kappa can be calculated. If the metric is low, the scoring standard should be revised before collecting more data.

6. Engineering Recommendations

1. Data governance: register the source, authorization status, and processing record for each batch of training data, and establish deletion and traceability mechanisms.
2. Annotation management: write clear annotation guidelines, set up pilot annotation and consistency checks, and regularly sample annotation quality.
3. Preference monitoring: track the reward model’s score distribution during training and intervene promptly when certain types of output are systematically overestimated.
4. Safety testing: conduct red-team testing against the model. Testing scope should be authorized, and discovered issues and fixes should be recorded.

7. Boundaries of the Conclusion

Differences in LLM capability result from multiple stages acting together. When discussing a single model’s ranking, evaluation conditions should be stated first, followed by an explanation of where the capability comes from. For engineering teams, more practical capabilities are building reproducible evaluation workflows, managing data and annotation quality, and monitoring bias during training.