# AI use cases for Mini Verl (DaoyuanLi2816/mini-verl) Repo: [DaoyuanLi2816/mini-verl](https://github.com/DaoyuanLi2816/mini-verl) | Use cases: 4 | Page: https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/ miniVERL is an independent Apache-2.0 Python project that runs verl-style PPO, GRPO and on-policy distillation training on one NVIDIA consumer GPU. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Run a first PPO experiment on one GPU: https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/first-local-ppo-grpo-run/ - Run an existing verl config on one GPU: https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/reuse-existing-verl-config/ - Distill a teacher model into a smaller student: https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/distill-teacher-into-student/ - Resume an interrupted run and export its outputs: https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/resume-and-export-local-run/ ## How to run a first PPO experiment on one GPU with Mini Verl Repo: [DaoyuanLi2816/mini-verl](https://github.com/DaoyuanLi2816/mini-verl) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Install miniVERL, sample prompts and train a Qwen3-0.6B PPO run with a length reward. **Who it is for:** An ML engineer with one NVIDIA GPU who wants a working reinforcement learning run before adapting it to their own task. **The problem:** Setting up verl-style reinforcement learning usually assumes a multi-GPU cluster, so a single-GPU user has no simple starting point. **The outcome:** A local PPO run that writes reward logs to rewards.jsonl and saves a resumable run under the chosen run ID. ### Steps 1. Install the matching CUDA-enabled PyTorch build from the PyTorch installer, then run `python -m pip install "miniverl[train,hydra]"`. 2. Create a small prompt set with `miniverl data sample --reward-profile target-length --rows 8 --out data/rl-prompts.parquet`. 3. Check the configuration without training using `miniverl run --example hydra-ppo --bind reward.provider=target_length --dry-run`. 4. Start the run with `miniverl run --example hydra-ppo --bind reward.provider=target_length --run-id local-ppo`. 5. Open rewards.jsonl in the run output to read the reward for each rollout. ### Prompt to paste into your agent ```text Work in the miniverl repo (DaoyuanLi2816/mini-verl) on this machine. Install it, sample 8 prompts into data/rl-prompts.parquet, run the hydra-ppo example with run ID , and report the path to rewards.jsonl. Verify that the dry run passes before the full run starts. ``` ### Caveat The README gives the workflow for a Qwen3-0.6B example on one NVIDIA GPU, and the CUDA PyTorch build must be chosen separately from the miniverl install. 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/daoyuanli2816-mini-verl/first-local-ppo-grpo-run/ ## How to run an existing verl config on one GPU with Mini Verl Repo: [DaoyuanLi2816/mini-verl](https://github.com/DaoyuanLi2816/mini-verl) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Point miniVERL at a verl config tree, switch to GRPO and check the run with a dry run. **Who it is for:** A team with a verl experiment that wants to test it on one consumer GPU without rewriting the config. **The problem:** A verl config written for a cluster cannot be tried locally without changing its structure and overrides. **The outcome:** A validated dry run of the verl config with GRPO and local GPU settings, ready for a real local run. ### Steps 1. Install miniVERL with `python -m pip install "miniverl[train,hydra]"`. 2. Locate the verl trainer config directory on disk, for example /path/to/verl/trainer/config. 3. Run `miniverl run --verl-config-path /path/to/verl/trainer/config --verl-config-name ppo_trainer --dry-run algorithm.adv_estimator=grpo actor_rollout_ref.rollout.n=8 trainer.n_gpus_per_node=8` and read the output. (inferred, check the README) 4. Read the direct-config guide to bind your data and reward function before a real run. ### Prompt to paste into your agent ```text Use the miniverl repo (DaoyuanLi2816/mini-verl) to dry-run a verl config at with the ppo_trainer config name and GRPO overrides. Report whether the dry run succeeds and list any overrides that failed. Verify that no training starts during the dry run. ``` ### Caveat The README says the tool supports documented verl configuration profiles, so configs outside those profiles may not map cleanly; check the compatibility matrix first. 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/daoyuanli2816-mini-verl/reuse-existing-verl-config/ ## How to distill a teacher model into a smaller student with Mini Verl Repo: [DaoyuanLi2816/mini-verl](https://github.com/DaoyuanLi2816/mini-verl) | Difficulty: Advanced | Time: 2 hours | Basis: From the README Train a student on its own rollouts using feedback from a teacher model with on-policy distillation. **Who it is for:** A researcher with one NVIDIA GPU who wants a smaller model to match a larger teacher on its own outputs. **The problem:** Teacher-student distillation on a single GPU is hard to set up and to inspect, and most tooling assumes large clusters. **The outcome:** A student adapter trained on its own rollouts with teacher targets, with visible losses and teacher targets in the logs. ### Steps 1. Read the OPD quickstart at docs/opd-quickstart.md to choose the teacher and student models. 2. Check memory needs with the hardware planning doc at docs/hardware-planning.md before you start. 3. Follow the on-policy distillation steps in docs/opd-quickstart.md and run a dry run first. 4. Review the logged rollouts, losses and teacher targets to confirm the student is learning. 5. Export the adapter and configuration using the hand-off steps in docs/verl-opd-scaleout.md. ### Prompt to paste into your agent ```text Using the miniverl repo (DaoyuanLi2816/mini-verl), follow docs/opd-quickstart.md to set up on-policy distillation with teacher and student on one GPU. Report the path to the exported adapter and the final teacher loss. Verify that the peak reserved VRAM stays within the GPU memory you have. ``` ### Caveat The README reports measured VRAM and results for specific Qwen3 and SmolLM2 workloads on an RTX 4080, so other models will need their own memory check. 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/daoyuanli2816-mini-verl/distill-teacher-into-student/ ## How to resume an interrupted run and export its outputs with Mini Verl Repo: [DaoyuanLi2816/mini-verl](https://github.com/DaoyuanLi2816/mini-verl) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Pick up a stopped training run from its checkpoint and export the adapter and configs for hand-off. **Who it is for:** A user whose local training run was interrupted and who needs to finish it and share the results with a larger setup. **The problem:** An interrupted single-GPU run can lose progress, and moving the result to a bigger setup needs clean exports. **The outcome:** A finished run with restored model, optimizer, data position and random state, plus exported adapters, datasets and a setup report. ### Steps 1. Find the run ID used for the interrupted run in the output directory. 2. Read the resume and export steps in docs/for-verl-users.md. 3. Resume the run from its latest checkpoint following the steps in that guide. 4. Export the adapter, datasets and configuration files with the steps in docs/verl-opd-scaleout.md. 5. Read the report of remaining setup in the export to see what a larger environment still needs. ### Prompt to paste into your agent ```text In the miniverl repo (DaoyuanLi2816/mini-verl), resume the run with ID from its latest checkpoint and export its adapter and configs to . Report the export paths and the remaining setup listed in the hand-off report. Verify that the resumed run continues from the saved step. ``` ### Caveat The README exact resume claim is shown for the OPD reference workload, so verify recovery on your own run 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/daoyuanli2816-mini-verl/resume-and-export-local-run/