[EXAMPLES] Update wordle to new openenv release

examples/scripts/openenv/wordle.py

@@ -30,25 +30,15 @@
 Setup:
 
 ```sh
-# uv pip install git+https://github.com/meta-pytorch/OpenEnv.git
-# Hotfix: https://github.com/huggingface/trl/pull/4740
-uv pip install git+https://github.com/meta-pytorch/OpenEnv.git@bf5e968286e0d49cdc03fd904d48faff4b15a437 openenv_core==0.1.1
+uv pip install git+https://huggingface.co/spaces/burtenshaw/wordle
 ```
 
-Usage:
-
-# Start the environment only if using --env-mode docker-local; In other modes, the env is automatically managed by the script.
-```sh
-docker run -d -p 8001:8001 registry.hf.space/burtenshaw-textarena:latest
-# or TEXTARENA_ENV_ID=Wordle-v0 TEXTARENA_NUM_PLAYERS=1 python -m src.envs.textarena_env.server.app
-```
-
-# Option 1: Colocated vLLM (1 GPU required)
+# Option 1: HF Spaces + Colocated vLLM (1 GPU required)
 ```sh
 python examples/scripts/openenv/wordle.py --vllm-mode colocate
 ```
 
-# Option 2: Separate vLLM server (2 GPUs required)
+# Option 2: HF Spaces + Separate vLLM server (2 GPUs required)
 
 # Spin up vLLM server (Terminal 1)
 ```sh
@@ -59,6 +49,19 @@
 ```sh
 CUDA_VISIBLE_DEVICES=1 python examples/scripts/openenv/wordle.py --vllm-mode server --vllm-server-url http://localhost:8000
 ```
+
+# Option 3: Local + Colocated vLLM (1 GPU required)
+
+Usage:
+
+# Start the environment only if using --env-mode docker-local; In other modes, the env is automatically managed by the script.
+```sh
+docker run -d -p 8001:8001 registry.hf.space/burtenshaw-wordle:latest
+```
+
+```sh
+python examples/scripts/openenv/wordle.py --vllm-mode colocate
+```
 """
 
 from __future__ import annotations
@@ -99,16 +102,8 @@ def parse_args() -> argparse.Namespace:
         default="Qwen/Qwen3-1.7B",
         help="Model identifier passed to GRPOTrainer for fine-tuning.",
     )
-    parser.add_argument("--env-host", type=str, default="0.0.0.0", help="Host for the environment server.")
-    parser.add_argument("--env-port", type=int, default=8001, help="Port for the environment server.")
-    parser.add_argument(
-        "--env-mode",
-        choices=["docker-local", "docker-image", "docker-hub", "space"],
-        default="docker-image",
-        help="Where to run the environment: 'docker-local' if already running locally, 'docker-image' to run from a Docker image, 'docker-hub' to run from Docker Hub, or 'space' to use a remote Space URL.",
-    )
     parser.add_argument(
-        "--env-image", type=str, default="textarena-env:latest", help="Docker image for the TextArena environment."
+        "--env-url", type=str, default="https://burtenshaw-wordle.hf.space", help="URL for the environment server."
     )
     parser.add_argument(
         "--system-prompt-path",
@@ -436,23 +431,7 @@ def main() -> None:
     tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_id)
     tokenizer.pad_token = tokenizer.eos_token
 
-    # Select environment mode
-    if args.env_mode == "docker-local":
-        env_url = f"http://{args.env_host}:{args.env_port}"
-        client = TextArenaEnv(base_url=env_url)
-        print(f"🌍 Using existing TextArena Environment (Docker) at: {env_url}")
-    elif args.env_mode == "docker-image":
-        client = TextArenaEnv.from_docker_image(args.env_image)
-        print("🌍 Using TextArena Environment (Docker) from local Image")
-    elif args.env_mode == "docker-hub":
-        client = TextArenaEnv.from_hub(args.env_image)
-        print("🌍 Using existing TextArena Environment (Docker) from Hub Image")
-    elif args.env_mode == "space":
-        env_url = args.env_host
-        client = TextArenaEnv(base_url=env_url)
-        print(f"🌍 Using Hugging Face Space environment at: {env_url}")
-    else:
-        raise ValueError(f"Unknown environment mode: {args.env_mode}")
+    client = TextArenaEnv(base_url=args.env_url)
 
     system_prompt = resolve_system_prompt(args.system_prompt_path)
 

trl/experimental/openenv/utils.py

@@ -23,7 +23,7 @@
 
 if is_vllm_available():
     from vllm import SamplingParams
-    from vllm.sampling_params import StructuredOutputsParams
+    from vllm.sampling_params import GuidedDecodingParams
 
 
 def _build_colocate_sampling_params(
@@ -33,17 +33,17 @@ def _build_colocate_sampling_params(
     logprobs: bool = True,
 ) -> SamplingParams:
     if trainer.structured_outputs_regex:
-        structured_outputs = StructuredOutputsParams(regex=trainer.structured_outputs_regex)
+        guided_decoding = GuidedDecodingParams(regex=trainer.structured_outputs_regex)
     else:
-        structured_outputs = None
+        guided_decoding = None
 
     generation_kwargs: dict[str, Any] = {
         "n": 1,
         "temperature": trainer.temperature,
         "top_k": trainer.top_k,
         "min_p": 0.0 if trainer.min_p is None else trainer.min_p,
         "max_tokens": trainer.max_completion_length,
-        "structured_outputs": structured_outputs,
+        "guided_decoding": guided_decoding,
     }
     if trainer.repetition_penalty is not None:
         generation_kwargs["repetition_penalty"] = trainer.repetition_penalty