vLLM support, more harness shortcuts and Readability Updates

.gitignore

@@ -91,13 +91,14 @@ ipython_config.py
 # PyPI config
 .pypirc
 
-# Project-local outputs
+# Project local
 *.log
 *.out
 *.pkl
 batches/
 wandb/
 checkpoints/
+models/
 experiments/
 rollout_results/
 outputs/
@@ -114,7 +115,7 @@ Megatron-LM/
 glm/
 
 # Generated documentation
-docs/_build/
+docs/
 /site
 
 # Dashboard frontend build artifacts

README.md

@@ -30,29 +30,41 @@
 
 #### 🟩 Install the **Rollout Server** (Polar): 
 ```bash
-uv venv
+uv venv --python 3.13
 uv pip install -e .
+source .venv/bin/activate
 ```
 
-#### 🟩 Install the **Inference Server** (SGLang):
+### 🟩 Install the **Inference Server** (SGLang or vLLM):
+
+Pick one (that your trainer supports). Avoid installing both under the same environment given dependency conflicts.
+
+**vLLM**
+```bash
+uv pip install vllm --torch-backend=auto
+```
+
+**SGLang**
 ```bash
-uv pip install --prerelease=allow sglang==0.5.10
+uv pip install --prerelease=allow sglang==0.5.10 torch==2.9.1+cu128
 bash scripts/patch/patch_sglang.sh
 ```
-The patch applies necessary TITO and prompt token id emission on pinned `sglang` version. We'll remove this once upstream supports go through. `vllm` integration is on the way.
+The patch applies necessary TITO and prompt token id emission on the pinned `sglang` version. We'll remove this once upstream support goes through.
+
+### 🟩 Install your favorite **Training Framework**:
 
-#### 🟩 Polar is trainer agnostic. So choice of **Trainer** and **Training Backend** are highly flexible given Polar's server boundaries.
+Polar is trainer agnostic. So choice of **Trainer** and **Training Backend** are highly flexible given Polar's HTTP server boundaries.
 
 Currently, we provide a demo-purpose [Slime](https://github.com/THUDM/slime) integration in [Slime bridge installation guide](src/slime_bridge/README.md#slime-installation).
 
 
-#### 🟩 (Optional) For SWE-bench official evaluation harness:
+#### (Optional) For SWE-bench official evaluation harness:
 
 ```bash
 uv pip install -e ".[swebench]"
 ```
 
-#### 🟩 (Optional) To enable **polar dashboard** UI, build the frontend once.
+#### (Optional) To enable **polar dashboard** UI, build the frontend once.
 
 ```bash
 cd web && npm install && npm run build
@@ -62,7 +74,7 @@ cd web && npm install && npm run build
 
 ## Usage Guide
 
-- ⭐ [Choose your Agent Harness](src/polar/agent/README.md): pick a built-in harness, or use the generic shell harness with wrapped agents.
+- ⭐ [Choose your Agent Harness](src/polar/agent/README.md): Express your agent using the generic `shell` harness, or pick a preset shortcut.
 - 🚀 [Trajectory Construction and Eval](src/polar/trajectory/README.md): See [builder](src/polar/trajectory/builder/README.md) and
   [evaluator](src/polar/trajectory/evaluator/README.md) guides for registered strategies.
 - 🔧 [Deployment Topology](src/polar/config/README.md): configure the Polar service.
@@ -110,7 +122,7 @@ Our development goal for **Polar** is low-intrusion and neutral, finding the low
 - [x] Slime bridge & RL example.
 - [x] CUA (VLM / VLA) Support.
 - [ ] More built-in evaluators (eg. self distillation with textual feedback).
-- [ ] vLLM dual inference support.
+- [x] vLLM dual inference support.
 - [ ] More trainer bridges (NemoRL, VERL, etc.).
 
 </td>

examples/calculator/README.md

@@ -1,110 +1,65 @@
 # Calculator Example
 
-This is a small end-to-end Polar rollout example. Each agent gets a tiny
-`calculator.py` file with parser stubs, edits it, and the evaluator runs
-`python3 test_calculator.py`.
+The smallest end-to-end Polar run. Each harness gets a tiny `calculator.py`
+with parser stubs, edits it, and the evaluator runs `python3 test_calculator.py`.
+Use it as a quick smoke test that rollout, gateway, runtime, harness execution,
+and evaluation all work together.
 
-Use this example when you want a quick local check that rollout, gateway,
-runtime setup, harness execution, and evaluation still work together.
+## Prerequisites
 
-The topology setup is used on 4 x B200 GPUs. Adjust based on your hardware.
+Install **Polar** and **vLLM** as described in the [top-level README](../../README.md#installation).
+This example uses 1 node 8×B200 — two vLLM servers (tensor-parallel 4 each).
+Adjust the setup and topology for your hardware.
 
-## What It Runs
+## Quick Start
 
-- rollout server on `:8080`
-- two gateway nodes on `:8100` and `:8101`
-- two local SGLang backends on `:8000` and `:8001`
-- one shared runtime image: `polar-localhost-calculator:latest`
-- six harnesses: `claude_code`, `codex`, `gemini_cli`, `opencode`, `pi`,
-  `qwen_code`
-
-The default scripts use Docker. Apptainer is also supported with
-`--backend apptainer`.
-
-## Setup
-
-From the repo root:
-
-```bash
-uv venv
-uv pip install -e .
-uv pip install --prerelease=allow sglang==0.5.10
-bash scripts/patch/patch_sglang.sh
-```
-
-Build the runtime image once:
+### 1. Build the runtime image (once)
 
 ```bash
 uv run python examples/calculator/build_image.py
 ```
 
-## Start Services
-
-Start two SGLang servers, one per GPU group:
+### 2. Start two vLLM servers
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 uv run python -m sglang.launch_server \
-  --model-path Qwen/Qwen3.5-4B \
-  --host 0.0.0.0 \
-  --port 8000 \
-  --tool-call-parser qwen3_coder \
-  --reasoning-parser qwen3 \
-  --mem-fraction-static 0.7 \
-  --context-length 262144 \
-  --trust-remote-code
-```
+CUDA_VISIBLE_DEVICES=0,1,2,3 uv run vllm serve Qwen/Qwen3.6-27B --port 8000 \
+  --tensor-parallel-size 4 --max-model-len 262144 \
+  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
 
-```bash
-CUDA_VISIBLE_DEVICES=1 uv run python -m sglang.launch_server \
-  --model-path Qwen/Qwen3.5-4B \
-  --host 0.0.0.0 \
-  --port 8001 \
-  --tool-call-parser qwen3_coder \
-  --reasoning-parser qwen3 \
-  --mem-fraction-static 0.7 \
-  --context-length 262144 \
-  --trust-remote-code
+CUDA_VISIBLE_DEVICES=4,5,6,7 uv run vllm serve Qwen/Qwen3.6-27B --port 8001 \
+  --tensor-parallel-size 4 --max-model-len 262144 \
+  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
 ```
 
-Start Polar:
+### 3. Start Polar Servers
 
 ```bash
 uv run polar serve_rollout -c examples/calculator/topology.yaml
-```
-
-```bash
 uv run polar serve_gateway -c examples/calculator/topology.yaml --node-id localhost-node-01
-```
-
-```bash
 uv run polar serve_gateway -c examples/calculator/topology.yaml --node-id localhost-node-02
 ```
 
-## Run
+### 4. Run
 
-Run every harness:
+Submits example harness at once and prints a reward comparison:
 
 ```bash
-uv run python examples/calculator/submit_all.py
+uv run python examples/calculator/run.py
 ```
 
-Run one harness:
+Use Apptainer instead of Docker with `--backend apptainer`.
 
-```bash
-uv run python examples/calculator/submit_calculator_task.py claude_code
-```
-
-Use Apptainer instead of Docker:
+### 5. (Optional) Watch in the dashboard
 
 ```bash
-uv run python examples/calculator/submit_all.py --backend apptainer
+uv run polar dashboard -c examples/calculator/topology.yaml
 ```
 
-Results are written under:
+Open <http://127.0.0.1:8090> to inspect live tasks, sessions, trajectories,
+and evaluations.
 
-```text
-examples/calculator/batches/<timestamp>/
-```
+<p align="center">
+  <img src="../../assets/dashboard_calculator.png" alt="Calculator dashboard" width="400">
+  <img src="../../assets/dashboard_trajectory.png" alt="Trajectory view" width="400">
+</p>
 
-Each harness directory contains `request.json`, `response.json`, and
-`summary.json`.