# AI use cases for Aibuildai LLM Posttrain Agent (aibuildai-inc/aibuildai-llm-posttrain-agent)

Repo: [aibuildai-inc/aibuildai-llm-posttrain-agent](https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent) | Use cases: 4 | Page: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/

A source-code release of a recursive self-improving agent that designs, runs, and iterates on post-training experiments for LLMs.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Post-train a base model on a math task overnight: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/post-train-gsm8k-example-model/
- Post-train a model for your own task and data: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/post-train-model-on-own-task-folder/
- Grade a post-trained model with the task's own grader: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/grade-post-trained-model-with-task-grader/
- Carry lessons from past runs into a new run: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/reuse-past-run-lessons-as-memory/

## How to post-train a base model on a math task overnight with Aibuildai LLM Posttrain Agent

Repo: [aibuildai-inc/aibuildai-llm-posttrain-agent](https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent) | Difficulty: Advanced | Time: 3 hours | Basis: From the README

Run the bundled GSM8K example to turn Qwen3-1.7B-Base into a grade-school math solver.

**Who it is for:** An ML engineer with one GPU who wants a working end-to-end post-training run before adapting the tool to their own data.

**The problem:** Setting up an autonomous post-training pipeline from scratch is slow, and it is hard to tell whether the agent works on a real task.

**The outcome:** A merged model directory (deliverable/final_model) trained against the GSM8K grader, plus a live workspace view of the run.

### Steps

1. Confirm the requirements, then run `python -m venv .venv && source .venv/bin/activate`
2. Run `pip install -r requirements.lock && pip install -e . --no-deps`
3. Authenticate with `claude auth login`, or run `export AIBUILDAI_API_KEY=sk-ant-...`
4. Build the example task with `bash examples/gsm8k-qwen3-1.7b-base/setup.sh ~/gsm8k-example`
5. Start the run with `aibuildai run ~/gsm8k-example/task.yaml` and open the printed workspace URL

### Prompt to paste into your agent

```text
In the aibuildai-llm-posttrain-agent repo, run the GSM8K example setup and then `aibuildai run` on the generated task.yaml. Expected output: a deliverable/final_model directory under the run home. Verify by confirming the run_config.json and deliverable folders exist before reporting success.
```

### Caveat

The README says the example takes about two hours on one GPU and the setup downloads about 4 GB, so the first run depends heavily on your hardware and network.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/post-train-gsm8k-example-model/

## How to post-train a model for your own task and data with Aibuildai LLM Posttrain Agent

Repo: [aibuildai-inc/aibuildai-llm-posttrain-agent](https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent) | Difficulty: Advanced | Time: 4 hours | Basis: From the README

Point a run at a folder with your task statement and data files, then start post-training.

**Who it is for:** A developer at a company who has proprietary data and a scoring script and wants the agent to tune a base model for that use case.

**The problem:** Hand-running many post-training experiments is expensive, and each change needs a fresh evaluation.

**The outcome:** A run that reads your task folder, writes its own score program, and produces a deliverable model package or task-defined result.

### Steps

1. Complete the install steps for the repo and set `AIBUILDAI_API_KEY` or sign in with `claude auth login`
2. Print the starter config with `aibuildai config > task.yaml`, or copy examples/gsm8k-qwen3-1.7b-base.yaml
3. Set `run.data_root` in task.yaml to your task folder with the statement, the data files, and any readme
4. Start the run with `aibuildai run task.yaml`
5. Open the printed workspace URL to follow the run and check the private and deliverable folders when it ends

### Prompt to paste into your agent

```text
Using the aibuildai-llm-posttrain-agent repo, create a task.yaml with run.data_root set to <task_folder>, then run `aibuildai run task.yaml`. Expected output: a deliverable directory and a score program under private/. Verify that the score program exists and reads the task folder before the run starts.
```

### Caveat

The README says a serious run needs larger budgets than the example, so plan for longer runtimes and check the cost limit, which is a soft limit.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/post-train-model-on-own-task-folder/

## How to grade a post-trained model with the task's own grader with Aibuildai LLM Posttrain Agent

Repo: [aibuildai-inc/aibuildai-llm-posttrain-agent](https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent) | Difficulty: Intermediate | Time: 3 hours | Basis: From the README

Score a deliverable model with the frozen grader that the run trained against.

**Who it is for:** A reviewer or ML engineer who needs an independent score for a model produced by an aibuildai run.

**The problem:** A run's reported progress is only useful if the final model is measured with the same grader and on the full test split.

**The outcome:** A grader result for the merged model on the full GSM8K test split (--limit -1), computed with the run's own Program environment.

### Steps

1. Set the run home with `H=~/gsm8k-example/playground/gsm8k-qwen3-1.7b-base/`
2. Confirm the deliverable exists at $H/deliverable/final_model
3. Run `$H/program-environment/env/bin/python ~/gsm8k-example/task/grade/evaluate.py --model-path $H/deliverable/final_model --limit -1` (inferred, check the README)
4. Compare the score with the base model score of 0.105 on the first 200 problems

### Prompt to paste into your agent

```text
For the aibuildai-llm-posttrain-agent GSM8K example, run the grader in the run's Program environment against deliverable/final_model with --limit -1. Expected output: a grader score for the full test split. Verify that the model path exists and that the score is reported on the full split before stating the result.
```

### Caveat

The README only gives the base model's score on the first 200 problems, so compare the trained model on the same split or note the difference in split size.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/grade-post-trained-model-with-task-grader/

## How to carry lessons from past runs into a new run with Aibuildai LLM Posttrain Agent

Repo: [aibuildai-inc/aibuildai-llm-posttrain-agent](https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent) | Difficulty: Advanced | Time: 4 hours | Basis: From the README

Fold earlier runs of a task into an editable memory document and inject it into new runs.

**Who it is for:** A team that has already run the agent on a task and wants the next run to start from what the earlier runs learned.

**The problem:** Each new run starts from scratch, so earlier findings about what worked are lost.

**The outcome:** An editable memory document built from past runs, used by every agent in the next run when memory is enabled.

### Steps

1. Run `aibuildai memorize task.yaml` to fold past runs of the task into a memory document
2. Edit the generated memory document to keep the findings you trust
3. Set `memory.enable` to true in task.yaml
4. Start the next run with `aibuildai run task.yaml`

### Prompt to paste into your agent

```text
In the aibuildai-llm-posttrain-agent repo, run `aibuildai memorize task.yaml`, then set memory.enable to true and run `aibuildai run task.yaml`. Expected output: a memory document and a new run that uses it. Verify that memory.enable is true in the config before starting the run.
```

### Caveat

The README does not say how the memory document is formatted or where it is stored, so check the generated file before relying on it.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/aibuildai-inc-aibuildai-llm-posttrain-agent/reuse-past-run-lessons-as-memory/