slime-rl-training

The framework Tsinghua's team actually used to build the GLM models, released openly: it runs the stage after pre-training where a model improves by being rewarded, and leaves the logic for producing practice material up to you.

WHEN TO USE

SCOPE

USE FOR

Run GRPO training on a GLM, Qwen3, DeepSeek, or Llama 3 model at scale.
Overlap rollout generation with training to cut GPU idle time in asynchronous mode.
Train a multi-turn agent that calls tools mid-conversation with a custom generate function.
Share GPUs between training and inference to reduce total memory footprint.

NOT FOR

Use miles instead for enterprise-grade stability features.
Use verl instead for flexible backend swapping.
Use torchforge instead for PyTorch-native abstractions.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (15)

slime: LLM Post-Training Framework for RL Scaling

slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation.

When to Use slime

Choose slime when you need:

Megatron-LM native training with SGLang inference
Custom data generation workflows with flexible data buffers
Training GLM, Qwen3, DeepSeek V3, or Llama 3 models
Research-grade framework with production backing (Z.ai)

Consider alternatives when:

You need enterprise-grade stability features → use miles
You want flexible backend swapping → use verl
You need PyTorch-native abstractions → use torchforge

Key Features

Training: Megatron-LM with full parallelism support (TP, PP, DP, SP)
Rollout: SGLang-based high-throughput generation with router
Data Buffer: Flexible prompt management and sample storage
Models: GLM-4.x, Qwen3, DeepSeek V3/R1, Llama 3

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    Data Buffer                          │
│ - Prompt initialization and management                  │
│ - Custom data generation and filtering                  │
│ - Rollout sample storage                                │
└─────────────┬───────────────────────────┬───────────────┘
              │                           │
┌─────────────▼───────────┐ ┌─────────────▼───────────────┐
│ Training (Megatron-LM)  │ │ Rollout (SGLang + Router)   │
│ - Actor model training  │ │ - Response generation       │
│ - Critic (optional)     │ │ - Reward/verifier output    │
│ - Weight sync to rollout│ │ - Multi-turn support        │
└─────────────────────────┘ └─────────────────────────────┘

Installation

# 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

From Source

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

Quick Start: GRPO Training

# 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[@]}

Workflow 1: Standard GRPO Training

Use this workflow for training reasoning models with group-relative advantages.

Prerequisites Checklist

Docker environment or Megatron-LM + SGLang installed
Model checkpoint (HuggingFace or Megatron format)
Training data in JSONL format

Step 1: Prepare Data

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

Or with chat format:

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

Step 2: Configure Model

Choose a pre-configured model script:

# 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

Step 3: Launch Training

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[@]}

Step 4: Monitor Training

Check TensorBoard: tensorboard --logdir outputs/
Verify reward curves are increasing
Monitor GPU utilization across nodes

Workflow 2: Asynchronous Training

Use async mode for higher throughput by overlapping rollout and training.

When to Use Async

Large models with long generation times
High GPU idle time in synchronous mode
Sufficient memory for buffering

Launch Async Training

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-Specific Parameters

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

Workflow 3: Multi-Turn Agentic Training

Use this workflow for training agents with tool use or multi-step reasoning.

Prerequisites

Custom generate function for multi-turn logic
Tool/environment interface

Step 1: Define Custom Generate Function

# 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

Step 2: Launch with Custom Function

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

See examples/search-r1/ for a complete multi-turn search example.

Configuration Reference

Three Argument Categories

slime uses three types of arguments:

1. Megatron Arguments (passed directly):

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

2. SGLang Arguments (prefixed with --sglang-):

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

3. slime Arguments:

# 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

Key Constraints

rollout_batch_size × n_samples_per_prompt = global_batch_size × num_steps_per_rollout

Example: 32 × 8 = 256 × 1

Data Buffer System

slime's data buffer enables flexible data management:

Basic Data Source

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

Buffered Data Source (Off-Policy)

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)

Common Issues and Solutions

Issue: SGLang Engine Crash

Symptoms: Inference engine dies mid-training

Solutions:

# Enable fault tolerance
--use-fault-tolerance

# Increase memory allocation
--sglang-mem-fraction-static 0.85

# Reduce batch size
--rollout-batch-size 16

Issue: Weight Sync Timeout

Symptoms: Training hangs after rollout

Solutions:

# Increase sync interval
--update-weights-interval 5

# Use colocated mode (no network transfer)
--colocate

Issue: OOM During Training

Symptoms: CUDA OOM in backward pass

Solutions:

# Enable gradient checkpointing
--recompute-activations

# Reduce micro-batch size
--micro-batch-size 1

# Enable sequence parallelism
--sequence-parallel

Issue: Slow Data Loading

Symptoms: GPU idle during data fetch

Solutions:

# Increase data workers
--num-data-workers 4

# Use streaming dataset
--streaming-data

Supported Models

Model Family	Configurations
GLM	GLM-4.5, GLM-4.6, GLM-4.7, GLM-Z1-9B
Qwen	Qwen3 (4B, 8B, 30B-A3B), Qwen3-MoE, Qwen2.5
DeepSeek	V3, V3.1, R1
Llama	Llama 3 (8B, 70B)
Others	Kimi K2, Moonlight-16B

Each model has pre-configured scripts in scripts/models/.

Advanced Topics

Co-location Mode

Share GPUs between training and inference to reduce memory:

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

Custom Reward Model

# 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

Evaluation Multi-Task

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

Resources

Documentation: https://thudm.github.io/slime/
GitHub: https://github.com/THUDM/slime
Blog: https://lmsys.org/blog/2025-07-09-slime/
Examples: See examples/ directory for 14+ worked examples

Local tool required

1 FOR YOU

01
Pull the slime Docker image
Run docker pull slimerl/slime:latest, start it with --gpus all --ipc=host --shm-size=16g, then pip install -e . --no-deps inside the container before launching training.
```
docker pull slimerl/slime:latest
```

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What's inside

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View on GitHub

slime: LLM Post-Training Framework for RL Scaling

When to Use slime

Key Features

Architecture Overview

Installation

From Source

Quick Start: GRPO Training

Workflow 1: Standard GRPO Training

Prerequisites Checklist

Step 1: Prepare Data

Step 2: Configure Model

Step 3: Launch Training

Step 4: Monitor Training

Workflow 2: Asynchronous Training

When to Use Async

Launch Async Training

Async-Specific Parameters

Workflow 3: Multi-Turn Agentic Training

Prerequisites

Step 1: Define Custom Generate Function

Step 2: Launch with Custom Function

Configuration Reference

Three Argument Categories

Key Constraints

Data Buffer System

Basic Data Source

Buffered Data Source (Off-Policy)

Common Issues and Solutions

Issue: SGLang Engine Crash

Issue: Weight Sync Timeout

Issue: OOM During Training

Issue: Slow Data Loading

Supported Models

Advanced Topics

Co-location Mode

Custom Reward Model

Evaluation Multi-Task

Resources

Local tool required

What's inside

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