GSM8K

GSM8K trains a grade-school math reasoning agent on the openai/gsm8k dataset with verl and Agent Lightning >=v1.0. This example runs in local mode and demonstrates support for two API styles:

1. Chat Completions API: the commonly used text-in/text-out API, where the agent sends structured chat messages and receives generated text.
2. Token-in/token-out Completions API: the agent sends prompt token IDs directly and receives generated token IDs.

Data Preparation

Download the dataset into ~/dataset/gsm8k:

hf download openai/gsm8k --repo-type dataset --local-dir ~/dataset/gsm8k

The example reads these files by default:

• ~/dataset/gsm8k/main/train-00000-of-00001.parquet
• ~/dataset/gsm8k/main/test-00000-of-00001.parquet

Training uses all samples from main/train. Validation uses 100 random samples from main/test with seed 42 by default.

Training

Make sure you have activated the project environment and installed the example dependencies:

source .venv/bin/activate
uv pip install \
    datasets \
    openai \
    httpx

Then start training:

source .venv/bin/activate
cd examples/gsm8k
bash run_local.sh

You can change the validation sample count or seed with:

bash run_local.sh --val-size 100 --seed 42

The local example uses ChatAgent with the standard Chat Completions API by default. To demonstrate the token-in/token-out Completions API, use CompletionAgent instead:

bash run_local.sh --api completion

In token-in/token-out mode, the agent tokenizes the prompt with the configured model tokenizer, sends prompt token IDs to the OpenAI-compatible Completions endpoint, receives response token IDs, and decodes them locally for answer evaluation.

run_local.sh starts agl-server, agl-controller, and Ray locally, and writes server/controller logs under /tmp/. When the script exits, it cleans up the local server, controller, and Ray process it started.