Quick Start Guide

Get Dressage Up and Running in Minutes

← Back to Main README · Prerequisites · Installation · Verify · Agent Modes · First Training · Configuration · Troubleshooting

📖 Overview

This guide walks you through installing Dressage, configuring your environment, and running your first agentic RL training job. By the end, you'll have a working training pipeline — either a whitebox agent (Python tool loop) or a blackbox agent (HTTP agent like opencode or codex) — producing real RL gradients from agent trajectories.

Dressage is designed to get you from zero to training with minimal configuration. The framework uses environment variables and dotted Python import paths for all slime hooks, so there's no config file authoring required.

📋 Prerequisites

Requirement	Version	Purpose	Notes
Python	≥ 3.10	Runtime	3.11+ recommended for performance
pip	Latest	Package installation	`pip install --upgrade pip`
Git	Any	Clone with submodules	Needs `--recurse-submodules` support
SGLang	Compatible with slime	Inference router	Must be running before training starts
GPU	CUDA-capable	Model inference and training	At least 1 GPU for inference, more for training

Optional Requirements

Requirement	Purpose	When Needed
bubblewrap	Local sandbox isolation via Linux namespaces	`DRESSAGE_SANDBOX_PROVIDER=local_bwrap`
opencode	Blackbox code-editing agent	`DRESSAGE_BLACKBOX_TYPE=opencode`
openclaw	Blackbox OpenClaw agent	`DRESSAGE_BLACKBOX_TYPE=openclaw`
Codex CLI	Blackbox Codex coding agent	`DRESSAGE_BLACKBOX_TYPE=codex`
E2B account	Remote cloud sandboxes	`DRESSAGE_SANDBOX_PROVIDER=e2b`
Docker	Reproducible local environment	Optional — for containerized setup
faiss-cpu	FAISS similarity search	HotpotQA recipe only
sentence-transformers	BGE embeddings	HotpotQA recipe only

1️⃣ Installation

Use the slime Base Image

slimerl/slime:nightly-dev-20260722a

Clone the Repository

git clone --recurse-submodules https://github.com/Accio-Lab/Dressage.git
cd Dressage

Tip

The --recurse-submodules flag automatically pulls the slime submodule. If you cloned without it, run git submodule update --init --recursive afterwards.

Install Dressage

# Install Dressage (core package)
pip install --no-build-isolation -e .

# Install BlackboxServer if you use blackbox agents
pip install --no-build-isolation blackbox_server/

# Install slime (upstream RL framework)
pip install --no-build-isolation -e slime/

Install Optional Dependencies

# 🧪 For running tests
pip install -e ".[test]"

# 🔍 For HotpotQA recipe (retrieval dependencies)
pip install faiss-cpu sentence-transformers numpy

Note

The root package currently defines only the test extra. Ray and E2B support are installed as core dependencies; BlackboxServer is installed from blackbox_server/ without editable mode because local bwrap sandboxes do not mount the BlackboxServer source directory.

2️⃣ Verify Installation

After installation, verify that the CLI entry points and test suite work:

# ✅ Check CLI entry points
dressage-proxy --help          # Proxy server
blackbox-server --help         # BlackboxServer, if blackbox_server/ was installed

# ✅ Check bwrap and local blackbox CLIs
dressage-local-bwrap-start --help
dressage-local-bwrap-status --help
dressage-local-bwrap-stop --help
dressage-local-blackbox-start --help
dressage-local-blackbox-status --help
dressage-local-blackbox-stop --help
dressage-blackbox-start --help
dressage-blackbox-status --help
dressage-blackbox-stop --help

# ✅ Run tests
pytest tests/ -x -q

Note

If dressage-proxy is not found, ensure that your Python environment's bin/ directory is in your PATH. For virtual environments: source venv/bin/activate.

To start the proxy manually, provide a tokenizer path:

dressage-proxy \
  --tokenizer-path /path/to/Qwen3.5-4B \
  --sglang-router-url http://<router-host>:8000

Important

For blackbox rollouts, DRESSAGE_PROXY_URL must be reachable from the sandbox. Avoid local-only URLs such as http://localhost:8800 unless the sandbox shares the same network namespace; set PROXY_PUBLIC_HOST or DRESSAGE_PROXY_URL to a sandbox-reachable host.

3️⃣ Choose Your Agent Mode

Dressage supports two agent paradigms. Choose based on your use case:

Whitebox — Python Tool Agents

Best for: custom tools, retrieval, API calls, lightweight environments. You write the agent logic in Python.

Your Python Agent
  └── self.chat(messages)          → Proxy → SGLang
  └── self.paddock.tool_call(...)  → Sandbox (shell, files)

Advantages:

Full control over agent prompting and tool logic
Easier to debug (Python stack traces)
Lower latency (no HTTP agent subprocess)
Direct access to LLM responses for custom processing

Get started:

# ALFWorld recipe (TextWorld navigation)
bash examples/scripts/run_alfworld_whitebox_agent_qwen3.5_4b.sh

# HotpotQA recipe (multi-hop retrieval)
bash examples/scripts/run_hotpotqa_whitebox_agent_qwen3.5_4b.sh

Blackbox — HTTP Agent Rollouts

Best for: real-world coding agents, complex environments, production agent frameworks like opencode/openclaw/claude_code/codex.

BlackboxServer (in sandbox)
  └── Backend Agent (opencode/openclaw/claude_code/codex)
      └── Agent's LLM calls → In-process Proxy → Dressage Proxy → SGLang
      └── Agent's tools → execution inside sandbox

For Codex rollouts, BlackboxServer creates an isolated sandbox-local CODEX_HOME and points Codex at the in-process proxy with a custom provider. Do not mount host ~/.codex into the sandbox; it may contain real auth tokens.

Advantages:

Use real-world agent frameworks without modification
Agent complexity is handled by the backend (no Python coding needed)
Full sandbox isolation (agent can't affect the host)
Closer to production behavior

Get started:

# Local bubblewrap sandbox
bash examples/scripts/run_blackbox_qwen3.5_4b_async_local.sh

# E2B remote sandbox
bash examples/scripts/run_blackbox_qwen3.5_4b_async_remote.sh

4️⃣ Run Your First Training

Option A: Whitebox Agent (ALFWorld)

The simplest way to start — no sandbox infrastructure needed:

# 1️⃣ Set agent mode
export DRESSAGE_PADDOCK_MODE=whitebox
export DRESSAGE_SANDBOX_PROVIDER=local_bwrap
export DRESSAGE_LOCAL_BWRAP_POOL_MODE=command_only

# 2️⃣ Run training
# The example script sources examples/scripts/default/dressage_env_defaults.sh
# and applies its helper defaults internally.
bash examples/scripts/run_alfworld_whitebox_agent_qwen3.5_4b.sh

Option B: Blackbox Agent (Local Bubblewrap)

For real-world coding agents with full sandbox isolation:

# 1️⃣ Set agent mode
export DRESSAGE_PADDOCK_MODE=blackbox
export DRESSAGE_SANDBOX_PROVIDER=local_bwrap
export DRESSAGE_LOCAL_BWRAP_POOL_MODE=blackbox
export DRESSAGE_BLACKBOX_TYPE=opencode
# Or use Codex CLI:
# export DRESSAGE_BLACKBOX_TYPE=codex

# 2️⃣ Start the bubblewrap sandbox pool
dressage-local-bwrap-start

# 3️⃣ Verify pool is healthy
dressage-local-bwrap-status

# 4️⃣ Run training
# The example script sources examples/scripts/default/dressage_env_defaults.sh
# and applies its helper defaults internally.
bash examples/scripts/run_blackbox_qwen3.5_4b_async_local.sh

# 5️⃣ Stop pool when done
dressage-local-bwrap-stop

Option C: Docker (All-in-One)

For a reproducible environment with all dependencies pre-installed:

# Build the Docker image
docker/build.sh

# Run with GPU access
docker/run.sh
# Starts with: --gpus all --network host --ipc host --privileged

Note

--privileged is required for bubblewrap inside Docker containers. The image includes bubblewrap, opencode, openclaw, Claude Code, Codex CLI, Dressage, BlackboxServer, and all dependencies. See docker/README.md for details.

⚙️ Configuration Reference

All Dressage configuration is via environment variables. Here's the complete reference organized by component:

Agent & Paddock

Variable	Values	Default	Description
`DRESSAGE_PADDOCK_MODE`	`blackbox` \| `whitebox`	`blackbox`	Agent interaction paradigm. Determines paddock subclass.
`DRESSAGE_PADDOCK_CLASS`	`module.Class`	—	Custom paddock class override for specialized lifecycle logic.

Sandbox

Variable	Values	Default	Description
`DRESSAGE_SANDBOX_PROVIDER`	`local_bwrap` \| `e2b`	`local_bwrap`	Sandbox backend. Determines where agent code runs.
`DRESSAGE_LOCAL_BWRAP_POOL_MODE`	`blackbox` \| `command_only`	Auto: `command_only` for whitebox, otherwise `blackbox`	Bwrap pool mode. Must match paddock mode.
`DRESSAGE_LOCAL_BWRAP_AUTO_START`	`0` \| `1`	`1` in example scripts	Auto-start the local bwrap pool from run scripts.
`DRESSAGE_LOCAL_BWRAP_RAY_NAMESPACE`	string	`dressage`	Ray namespace for local bwrap actors.
`DRESSAGE_LOCAL_BWRAP_MANAGER_NAME`	string	`dressage_local_bwrap_manager`	Ray actor name for the bwrap pool manager.
`DRESSAGE_LOCAL_BWRAP_TOTAL_SERVERS`	int	computed by scripts	Total bwrap slots across the Ray cluster.
`DRESSAGE_LOCAL_BWRAP_BASE_PORT`	int	`31000`	Base port for local BlackboxServer slots.
`DRESSAGE_BLACKBOX_SLOTS_PER_NODE`	int	script-specific	Local bwrap slot count per Ray node.
`DRESSAGE_BLACKBOX_RUNNER_MODE`	`bwrap` \| `bubblewrap`	`bwrap`	Local blackbox runner mode.
`DRESSAGE_BLACKBOX_BWRAP_BIN`	path	`bwrap`	Bubblewrap binary used by local slots.
`DRESSAGE_LOCAL_BWRAP_DESTROY_ACTORS_ON_STOP`	`0` \| `1`	`1`	Destroy Ray actors when stopping the pool.
`DRESSAGE_LOCAL_BWRAP_CLEANUP_ON_EXIT`	`0` \| `1`	`1`	Stop the local bwrap pool on example-script exit.
`DRESSAGE_BLACKBOX_PRESERVE_SESSION_ARTIFACTS`	`0` \| `1`	`0`	Preserve sandbox filesystem after sessions for debugging.

Proxy

Variable	Values	Default	Description
`DRESSAGE_PROXY_URL`	URL	`http://${PROXY_PUBLIC_HOST}:${PROXY_PORT}` in scripts	Proxy server endpoint. Must be reachable from sandboxes.
`TOKEN_BUILD_MODE`	`tito` \| `snapshot`	`tito` in scripts	Script helper passed to proxy `--token-build-mode`.
`TITO_MODEL`	string	`qwen3_5` in scripts	Script helper passed to proxy `--tito-model`.
`DRESSAGE_PROXY_MAX_STEPS_PER_SESSION`	int	`0` (unlimited)	Returns HTTP 400 before the next proxy generation once the session already has this many steps.

Partial Rollout

Variable	Values	Default	Description
`DRESSAGE_PROXY_PAUSE_AROUND_WEIGHT_UPDATE`	`0` \| `1`	`1`	Enable proxy pause/resume around weight updates.
`DRESSAGE_PROXY_PAUSE_REQUIRED`	`0` \| `1`	—	Require pause to succeed (fail if proxy unreachable).
`DRESSAGE_PROXY_PAUSE_TIMEOUT_SEC`	int	`300`	Timeout for pause confirmation in seconds.

Blackbox & BlackboxServer

Variable	Values	Default	Description
`DRESSAGE_BLACKBOX_TYPE`	`opencode` \| `openclaw` \| `claude_code` \| `codex`	`opencode`	Backend agent type.
`DRESSAGE_BLACKBOX_MAX_STEPS`	int	—	Positive int forwarded to `backend_options.proxy.max_steps`; set `0` to disable the backend proxy step limit.
`DRESSAGE_BLACKBOX_COMPACT_THRESHOLD`	int	—	Positive value no greater than the context window; controls backend compaction reserve sizing.
`BBS_HOST`	host	`0.0.0.0`	BlackboxServer bind host.
`BBS_PORT`	port	`23456`	BlackboxServer bind port.
`BBS_BACKEND_TIMEOUT`	float	`960.0`	Agent call timeout in seconds (16 min).
`BBS_EXECUTE_CMD_TIMEOUT`	float	`600.0`	Shell command timeout in seconds (10 min).
`BBS_ROUTER_TIMEOUT`	int	`600000`	Timeout for requests from BlackboxServer's in-process proxy to the upstream router.
`BBS_SHUTDOWN_TIMEOUT`	float	`30.0`	Shutdown grace period in seconds.
`BBS_RUNTIME_HEALTH_CHECK_INTERVAL`	float	`10.0`	Interval between backend runtime health checks.
`BBS_RUNTIME_HEALTH_CHECK_RETRIES`	int	`3`	Runtime health-check retry count.
`BBS_RUNTIME_HEALTH_CHECK_RETRY_DELAY`	float	`0.5`	Delay between runtime health-check retries.
`OPENCODE_BIN`	path	`opencode`	Path to the opencode binary.
`OPENCLAW_BIN`	path	`openclaw`	Path to the openclaw binary.
`CLAUDE_CODE_BIN`	path	`claude`	Path to the Claude Code binary.
`CODEX_BIN`	path	`codex`	Path to the Codex CLI binary.

Reward

Variable	Values	Default	Description
`DRESSAGE_REWARD_MODULES`	comma-separated	—	Reward function modules to load (e.g., `my_project.rewards`).

Async Rollout

Variable	Values	Default	Description
`DRESSAGE_PARTIAL_ROLLOUT_TARGET_GROUPS`	int	—	Override target groups for partial async mode.
`DRESSAGE_PARTIAL_ROLLOUT_TARGET_SAMPLES`	int	—	Override partial async readiness by sample count.
`DRESSAGE_ASYNC_MAX_ACTIVE_GROUPS`	int	—	Cap active async prompt groups.
`DRESSAGE_ASYNC_OUTPUT_QUEUE_SIZE`	int	`1000`	Async worker output queue size.
`DRESSAGE_ASYNC_WORKER_STOP_TIMEOUT_SEC`	float	`300`	Timeout for stopping async workers.
`DRESSAGE_ROLLOUT_MAX_RETRIES`	int	`2`	Per-group rollout retry limit.
`DRESSAGE_ASYNC_NO_PROGRESS_WARN_SEC`	float	`600`	No-progress warning threshold.
`DRESSAGE_ASYNC_MAX_DROPPED_FAILED_GROUPS`	int	—	Maximum failed groups that can be dropped by async rollout.
`DRESSAGE_ALLOW_EMPTY_TRAIN_BATCH`	`0` \| `1`	`0`	Allow empty train batches after failures.

Logging & Debugging

Variable	Values	Default	Description
`DRESSAGE_TRAJECTORY_PAYLOAD_LOG_DIR`	path	—	Directory for trajectory payload logs.
`DRESSAGE_TRAJECTORY_ERROR_LOG_DIR`	path	—	Directory for trajectory error logs.
`DRESSAGE_LOG_WRITE_MODE`	`background` \| `await`	`background`	Log write mode. `background` is faster; `await` ensures logs are flushed before proceeding.

🔧 Slime Wiring Cheatsheet

Quick-reference examples for common training configurations:

Blackbox + Sync + GRPO

The simplest blackbox configuration: synchronous rollout with GRPO advantage estimation.

python3 -m slime.train \
  --custom-generate-function-path \
    dressage.rollout.generate.blackbox_dispatch.generate \
  --rollout-function-path \
    dressage.rollout.sync_rollout.generate_rollout_sync \
  --custom-convert-samples-to-train-data-path \
    dressage.rollout.convert_samples.convert_samples_to_train_data \
  --custom-reward-post-process-path \
    dressage.training.reward_post_process.reward_post_process \
  --custom-rm-path \
    dressage.reward.custom_rm.custom_rm \
  --data-source-path \
    dressage.rollout.data_source.DressageDataSource \
  --custom-rollout-log-function-path \
    dressage.rollout.log_rollout.log_rollout_data \
  --advantage-estimator grpo

Whitebox + Fully Async

Whitebox agents with fully async scheduling use slime's async entry point.

export DRESSAGE_REWARD_MODULES=my_recipe.reward

cd slime
python3 train_async.py \
  --custom-generate-function-path \
    my_recipe.agent.generate \
  --rollout-function-path \
    dressage.rollout.fully_async_rollout.generate_rollout_fully_async \
  --custom-convert-samples-to-train-data-path \
    dressage.rollout.convert_samples.convert_samples_to_train_data \
  --custom-reward-post-process-path \
    dressage.training.reward_post_process.reward_post_process \
  --custom-rm-path \
    dressage.reward.custom_rm.custom_rm \
  --data-source-path \
    dressage.rollout.data_source.DressageDataSource \
  --advantage-estimator grpo

Select a registered reward with sample.metadata["reward_fn"]; omitted values fall back to default.

Whitebox + Sync (Simplest)

The simplest configuration for development and debugging. Synchronous rollout — all samples complete before training.

cd slime
python3 train.py \
  --custom-generate-function-path \
    dressage.recipes.alfworld.agent_whitebox.generate \
  --rollout-function-path \
    dressage.rollout.sync_rollout.generate_rollout_sync \
  --custom-convert-samples-to-train-data-path \
    dressage.rollout.convert_samples.convert_samples_to_train_data \
  --custom-rm-path \
    dressage.reward.custom_rm.custom_rm \
  --advantage-estimator grpo

For example, set DRESSAGE_REWARD_MODULES=dressage.recipes.alfworld.reward so the custom reward registry loads the ALFWorld reward.

🆘 Troubleshooting

Common issues and their solutions:

Issue	Symptoms	Solution
Proxy connection refused	`ConnectionRefusedError` on chat completions	Check `DRESSAGE_PROXY_URL` and ensure the proxy server is running (`dressage-proxy`).
Bwrap slot unavailable	Rollout hangs waiting for sandbox	Run `dressage-local-bwrap-status` to check pool health. Ensure enough slots are provisioned.
Mode mismatch error	`ValueError` at startup about paddock/pool mode	Ensure `DRESSAGE_PADDOCK_MODE` matches `DRESSAGE_LOCAL_BWRAP_POOL_MODE` (blackbox↔blackbox, whitebox↔command_only).
Backend not found	`FileNotFoundError` for opencode/openclaw/claude binary	Set `OPENCODE_BIN`, `OPENCLAW_BIN`, or `CLAUDE_CODE_BIN` to the full path of the binary.
TITO failure	Warning about `tito_incremental_tokenization_failed`	Check model compatibility — TITO currently supports `qwen3_5` only. Failure triggers safe segment boundary.
Pause timeout	`TimeoutError` during weight update	Increase `DRESSAGE_PROXY_PAUSE_TIMEOUT_SEC`. Default 300s may not be enough for large batches.
Session desync	BlackboxServer reports `desynced` state	Agent process may have crashed. Check sandbox logs. Session will be aborted and retried automatically.
Docker bwrap fails	Bubblewrap errors inside container	Ensure Docker is running with `--privileged` flag. Required for Linux namespace operations inside containers.
Zero reward	All trajectories get reward=0.0	Check reward function registration. Ensure `DRESSAGE_REWARD_MODULES` includes your module and `@register_reward` decorator is applied.

Tip

Enable trajectory payload logging for debugging: export DRESSAGE_TRAJECTORY_PAYLOAD_LOG_DIR=/tmp/dressage_logs. This saves full trajectory data to disk for offline inspection.

📚 Next Steps

After your first successful training run, explore these resources to go deeper:

Resource	Description
Proxy	Deep dive into trajectory recording, TITO, segment boundaries, and routing replay
Paddock	Understand environment interaction, blackbox vs whitebox, factory pattern
Sandbox	Configure isolation backends — bwrap pools and E2B cloud
BlackboxServer	Set up blackbox agent rollouts, understand the adapter protocol
Rollout	Customize slime integration, async modes, reward registry
Training	Multi-segment training, prompt-equal scaling, partial rollout
Recipes	Build your own agents with ALFWorld and HotpotQA as templates

← Recipes · Back to Main README

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Uh oh!

Quick Start Guide

📖 Overview

📋 Prerequisites

1️⃣ Installation

Use the slime Base Image

Clone the Repository

Install Dressage

Install Optional Dependencies

2️⃣ Verify Installation

3️⃣ Choose Your Agent Mode

Whitebox — Python Tool Agents

Blackbox — HTTP Agent Rollouts

4️⃣ Run Your First Training

Option A: Whitebox Agent (ALFWorld)

Option B: Blackbox Agent (Local Bubblewrap)

Option C: Docker (All-in-One)

⚙️ Configuration Reference

🔧 Slime Wiring Cheatsheet

🆘 Troubleshooting

📚 Next Steps

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Quick Start Guide

📖 Overview

📋 Prerequisites

1️⃣ Installation

Use the slime Base Image

Clone the Repository

Install Dressage

Install Optional Dependencies

2️⃣ Verify Installation

3️⃣ Choose Your Agent Mode

Whitebox — Python Tool Agents

Blackbox — HTTP Agent Rollouts

4️⃣ Run Your First Training

Option A: Whitebox Agent (ALFWorld)

Option B: Blackbox Agent (Local Bubblewrap)

Option C: Docker (All-in-One)

⚙️ Configuration Reference

🔧 Slime Wiring Cheatsheet

🆘 Troubleshooting

📚 Next Steps