slime-rl-training

# Recommended: Docker
docker pull slimerl/slime:latest
docker run --rm --gpus all --ipc=host --shm-size=16g \
  -it slimerl/slime:latest /bin/bash
 
# Inside container
cd /root/slime && pip install -e . --no-deps

git clone https://github.com/THUDM/slime.git
cd slime
pip install -r requirements.txt
pip install -e .

# Source model configuration
source scripts/models/qwen3-4B.sh
 
# Launch training
python train.py \
    --actor-num-nodes 1 \
    --actor-num-gpus-per-node 4 \
    --rollout-num-gpus 4 \
    --advantage-estimator grpo \
    --use-kl-loss --kl-loss-coef 0.001 \
    --rollout-batch-size 32 \
    --n-samples-per-prompt 8 \
    --global-batch-size 256 \
    --num-rollout 3000 \
    --prompt-data /path/to/data.jsonl \
    ${MODEL_ARGS[@]} ${CKPT_ARGS[@]}

# data.jsonl format
{"prompt": "What is 2 + 2?", "label": "4"}
{"prompt": "Solve: 3x = 12", "label": "x = 4"}

{
    "prompt": [
        {"role": "system", "content": "You are a math tutor."},
        {"role": "user", "content": "What is 15 + 27?"}
    ],
    "label": "42"
}

# List available models
ls scripts/models/
# glm4-9B.sh, qwen3-4B.sh, qwen3-30B-A3B.sh, deepseek-v3.sh, llama3-8B.sh, ...
 
# Source your model
source scripts/models/qwen3-4B.sh

python train.py \
    --actor-num-nodes 1 \
    --actor-num-gpus-per-node 8 \
    --rollout-num-gpus 8 \
    --advantage-estimator grpo \
    --use-kl-loss \
    --kl-loss-coef 0.001 \
    --prompt-data /path/to/train.jsonl \
    --input-key prompt \
    --label-key label \
    --apply-chat-template \
    --rollout-batch-size 32 \
    --n-samples-per-prompt 8 \
    --global-batch-size 256 \
    --num-rollout 3000 \
    --save-interval 100 \
    --eval-interval 50 \
    ${MODEL_ARGS[@]}

python train_async.py \
    --actor-num-nodes 1 \
    --actor-num-gpus-per-node 8 \
    --rollout-num-gpus 8 \
    --advantage-estimator grpo \
    --async-buffer-size 4 \
    --prompt-data /path/to/train.jsonl \
    ${MODEL_ARGS[@]}

--async-buffer-size 4        # Number of rollouts to buffer
--update-weights-interval 2  # Sync weights every N rollouts

# custom_generate.py
async def custom_generate(args, samples, evaluation=False):
    """Multi-turn generation with tool calling."""
    for sample in samples:
        conversation = sample.prompt
 
        for turn in range(args.max_turns):
            # Generate response
            response = await generate_single(conversation)
 
            # Check for tool call
            tool_call = extract_tool_call(response)
            if tool_call:
                tool_result = execute_tool(tool_call)
                conversation.append({"role": "assistant", "content": response})
                conversation.append({"role": "tool", "content": tool_result})
            else:
                break
 
        sample.response = response
        sample.reward = compute_reward(sample)
 
    return samples

python train.py \
    --custom-generate-function-path custom_generate.py \
    --max-turns 5 \
    --prompt-data /path/to/agent_data.jsonl \
    ${MODEL_ARGS[@]}

--tensor-model-parallel-size 2
--pipeline-model-parallel-size 1
--num-layers 32
--hidden-size 4096

--sglang-mem-fraction-static 0.8
--sglang-context-length 8192
--sglang-log-level INFO

# Resource allocation
--actor-num-nodes 1
--actor-num-gpus-per-node 8
--rollout-num-gpus 8
--colocate  # Share GPUs between training/inference
 
# Data
--prompt-data /path/to/data.jsonl
--input-key prompt
--label-key label
 
# Training loop
--num-rollout 3000
--rollout-batch-size 32
--n-samples-per-prompt 8
--global-batch-size 256
 
# Algorithm
--advantage-estimator grpo  # or: gspo, ppo, reinforce_plus_plus
--use-kl-loss
--kl-loss-coef 0.001

rollout_batch_size × n_samples_per_prompt = global_batch_size × num_steps_per_rollout

class RolloutDataSource:
    def get_samples(self, num_samples):
        """Fetch prompts from dataset."""
        return self.dataset.sample(num_samples)
 
    def add_samples(self, samples):
        """Called after generation (no-op by default)."""
        pass

class RolloutDataSourceWithBuffer(RolloutDataSource):
    def __init__(self):
        self.buffer = []
 
    def add_samples(self, samples):
        """Store generated samples for reuse."""
        self.buffer.extend(samples)
 
    def buffer_filter(self, args, buffer, num_samples):
        """Custom selection logic (prioritized, stratified, etc.)."""
        return select_best(buffer, num_samples)

# Enable fault tolerance
--use-fault-tolerance
 
# Increase memory allocation
--sglang-mem-fraction-static 0.85
 
# Reduce batch size
--rollout-batch-size 16

# Increase sync interval
--update-weights-interval 5
 
# Use colocated mode (no network transfer)
--colocate

# Enable gradient checkpointing
--recompute-activations
 
# Reduce micro-batch size
--micro-batch-size 1
 
# Enable sequence parallelism
--sequence-parallel

# Increase data workers
--num-data-workers 4
 
# Use streaming dataset
--streaming-data

python train.py \
    --colocate \
    --actor-num-gpus-per-node 8 \
    --sglang-mem-fraction-static 0.4 \
    ${MODEL_ARGS[@]}

# custom_rm.py
class CustomRewardModel:
    def __init__(self, model_path):
        self.model = load_model(model_path)
 
    def compute_reward(self, prompts, responses):
        inputs = self.tokenize(prompts, responses)
        scores = self.model(inputs)
        return scores.tolist()

--custom-rm-path custom_rm.py

--eval-prompt-data aime /path/to/aime.jsonl \
--eval-prompt-data gsm8k /path/to/gsm8k.jsonl \
--n-samples-per-eval-prompt 16