Work in progress — interfaces will change.

• 15/07/2025: v0.2.0 A2C, runtime object, environment scheduling
• 23/06/2025: Early release of the pip package (pip install unstable-rl)
• 22/06/2025: Early release of the code base

UnstableBaselines is an Async-, Online-, Multi-Agent RL library focused on simplicity and hackability. Since multiple recent papers showed the sufficiency of LoRA for reasoning tuning, and the fact that opponent sampling for self-play strategies beyond mirror self-play work best when using LoRA weights (since vLLM allows for hot-swapping), we built UnstableBaselines as a LoRA first RL library. We tried to keep the code as straight forward as possible. It is currently around 1.2K lines long and semi-readable. The main focus of unstable-baselines is to enable fast prototyping/research. For something a bit more production ready we recommend to use oat or verifiers.

Lines of Code per Release
-------------------------
0.1.0  | ######################     1,144       -> initial release
0.2.0  | ########################   1,269       -> added A2C, runtime object, environment scheduling

• Asynchronous collection & learning – actors generate data while learners train.
• Multi‑agent, multi‑turn focus with self‑play or fixed opponents.
• LoRA‑first fine‑tuning workflow for fast, lightweight updates.
• Composable reward transforms at step, game, and sampling stages.

 ┌─────────┐ ┌─────────┐             ┌────────────┐
 │   Env   │ │  Model  │ Get Models  │    Model   │
 │ Sampler │ │ Sampler │◀─────────── │  Registry  │
 └─────────┘ └─────────┘             └────────────┘ 
      │          │                         ▲
      │Sample    │Sample                   │Push
      │Env       │Opponent                 │Checkpoint 
      ▼          ▼                         │
    ┌───────────────┐              ┌───────────────┐
    │               │              │               │
    │ GameScheduler │              │    Learner    │
    │               │              │               │
    └───────────────┘              └───────────────┘
           ▲ │                            ▲ │ 
           │ │ Sample           If enough │ │ Check if enough
    Update │ │ GameSpec        data, pull │ │ data for training
           │ │             the next batch │ │ is available
           │ ▼                            │ ▼
    ┌───────────────┐               ┌────────────┐
    │               │      Send     │            │
    │   Collector   │──────────────▶│   Buffer   │
    │               │ Trajectories  │            │
    └───────────────┘               └────────────┘
           ▲ │
           │ │ Maintain
    return │ │ Pool of 
Trajectory │ │ n parallel
           │ │ workers
           │ ▼
     ┌─────────────┐
     │  run_game() │
     │  train/eval │
     └─────────────┘

install UnstableBaselines

pip3 install unstable-rl

To get you started, in this short example we will run you through the process of training Qwen3-1.7B-Base via mirror self-play on SimpleTak and evaluating it against google/gemini-2.0-flash-lite-001 on SimpleTak and KuhnPoker. We will be running the experiments on 3xRTX6000 ada. If you are limited to 24gb of vRam, you can reduce the MAX_TRAIN_SEQ_LEN to around 2500; this means that the model will only be trained on the first 2500 prompt+answer tokens, but can still generate answer that are longer than that. Since (in our experience) models tend to shorten their reasoning throughout training, this works very well.

import time, ray, unstable
import unstable.reward_transformations as retra
MODEL_NAME = "Qwen/Qwen3-1.7B-Base"
MAX_TRAIN_SEQ_LEN = None
MAX_GENERATION_LENGTH = 4096 
lora_config = {
    "lora_rank": 32, "lora_alpha": 32, "lora_dropout": 0.0,
    "target_modules": ["q_proj","k_proj","v_proj","o_proj","gate_proj", "up_proj","down_proj"]
}
vllm_config = {
    "model_name": MODEL_NAME, "temperature": 0.6, "max_tokens": MAX_GENERATION_LENGTH,
    "max_parallel_seq": 128, "max_loras": 8, "lora_config": lora_config,
    "max_model_len": 8192
}
# Ray init
ray.init(namespace="unstable")  
# initialize environment scheduler
env_sampler = unstable.samplers.env_samplers.UniformRandomEnvSampler(
    train_env_specs=[
        unstable.TrainEnvSpec(env_id="SimpleTak-v0-train", num_players=2, num_actors=2, prompt_template="qwen3-zs"), # if num_players == num_actors, it's mirror self-play and no opponents will be sampled
    ],
    eval_env_specs=[
        unstable.EvalEnvSpec(env_id="SimpleTak-v0-train", num_players=2, prompt_template="qwen3-zs"),
        unstable.EvalEnvSpec(env_id="KuhnPoker-v0-train", num_players=2, prompt_template="qwen3-zs"),
])
# Tracker
tracker = unstable.Tracker.options(name="Tracker").remote(
    run_name=f"Test-{MODEL_NAME.split('/')[-1]}-{env_sampler.env_list()}-{int(time.time())}", 
    wandb_project="UnstableBaselines"
) 
# initialize model registry
model_registry = unstable.ModelRegistry.options(name="ModelRegistry").remote(tracker=tracker)
ray.get(model_registry.add_checkpoint.remote(uid="base", path=None, iteration=0))
ray.get(model_registry.add_fixed.remote(name="google/gemini-2.0-flash-lite-001"))
# initialize model sampler
model_sampler = unstable.samplers.model_samplers.BaseModelSampler(model_registry=model_registry) 
# build game scheduler
game_scheduler = unstable.GameScheduler.options(name="GameScheduler").remote(model_sampler=model_sampler, env_sampler=env_sampler, logging_dir=ray.get(tracker.get_log_dir.remote()))
# Data Buffer
step_buffer = unstable.StepBuffer.options(name="Buffer").remote(
    max_buffer_size=384*2, tracker=tracker,
    final_reward_transformation=retra.ComposeFinalRewardTransforms([retra.RoleAdvantageByEnvFormatter()]),
    step_reward_transformation=retra.ComposeStepRewardTransforms([retra.RewardForFormat(1.5), retra.PenaltyForInvalidMove(1.0, -1.0)]),
    sampling_reward_transformation=retra.ComposeSamplingRewardTransforms([retra.NormalizeRewardsByEnv(True)]),
)
# initialize the collector
collector = unstable.Collector.options(name="Collector").remote(
    vllm_config=vllm_config, tracker=tracker, buffer=step_buffer, game_scheduler=game_scheduler,
)
# initialize the learner
learner = unstable.REINFORCELearner.options(num_gpus=1, name="Learner").remote(
    model_name=MODEL_NAME,
    lora_cfg=lora_config,
    batch_size=384,
    mini_batch_size=1,
    learning_rate=1e-5,
    grad_clip=0.2,
    buffer=step_buffer,
    tracker=tracker,
    model_registry=model_registry,
    activation_checkpointing=True,
    gradient_checkpointing=True,
    use_trainer_cache=False
)
ray.get(learner.initialize_algorithm.remote(max_train_len=MAX_TRAIN_SEQ_LEN, max_generation_len=MAX_GENERATION_LENGTH))
try:
    collector.collect.remote(num_train_workers=384, num_eval_workers=16)
    ray.get(learner.train.remote(200))
finally:
    ray.kill(collector, no_restart=True)
    ray.shutdown()

In a Nutshell, the Collector will maintain 384 and 16 in parallel running collection and evaluation games (respectively). Whenever a game finishes, the trajectory is passed to the StepBuffer and a new game is started. The StepBuffer splits each trajectory into steps and applies the specified reward transformations (on the game and step level first; and batch level once the Learner pulls the next batch).

The Learner will periodically (once every 0.2 seconds) check if the StepBuffer has accumulated enough data for training. If so, it'll request a full training batch from the StepBuffer, train on the data, and push the new set of LoRA weights to the ModelPool.

The Collector will keep collecting episodes until the Learner tells it to stop (in this case, after 200 update steps).

If you want to monitor key metrics (in addition to logging them via W&B) during training you can run the following command in a seperate terminal:

unstable-terminal

The rendered interface will currently look something like this: (please not that it might change in the future as UnstableBaselines is very much still under development)

The .gif doesn't do it justice, looks nice when you run it yourself haha.

Since we set num_eval_workers=16, throughout training there are always 16 eval games running in parallel (using the most recent lora checkpoint). Running 200 learner steps took a total of ~12h on the 3xRTX6000 ada setup we used.

As can be seen in the plots the Win-Rate against a fixed opponent (in this case google/gemini-2.0-flash-lite-001) improves significantly for both the training and evaluation environment, showing that at least some of learned reasoning patterns generalize to other tasks and problems.

Developed in partnership with PlasticLabs.

We built this code-base as part of our research on self-play for reasoning models on text based games. We hope to finish and release both papers (one focused on the paradigm and one focused on the "scaling-laws" and analysis thereof) within the next couple of weeks!

If you use UnstableBaselines in your research, please cite:

@software{guertler_leon_2025_15719271,
  author={Guertler, Leon and Grams, Tim and Liu, Zichen and Cheng, Bobby},
  title={{UnstableBaselines}},
  month=jun,
  year=2025,
  publisher={Zenodo},
  version={0.1.0},
  doi={10.5281/zenodo.15719271},
  url={https://doi.org/10.5281/zenodo.15719271}
}