arc-agi resource server

resources_servers/arc_agi/README.md

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+# ARC-AGI resources server
+
+launch local vllm server
+```bash
+vllm serve Qwen/Qwen3-30B-A3B \
+    --dtype auto \
+    --tensor-parallel-size 8 \
+    --gpu-memory-utilization 0.9 \
+    --enable-auto-tool-choice --tool-call-parser hermes \
+    --host 0.0.0.0 \
+    --port 10240
+```
+
+Start ARC-AGI environment:
+```bash
+ng_run "+config_paths=[resources_servers/arc_agi/configs/arc_agi.yaml,responses_api_models/vllm_model/configs/vllm_model.yaml]"
+```
+
+or ARC-AGI-2 environment:
+```bash
+ng_run "+config_paths=[resources_servers/arc_agi/configs/arc_agi_2.yaml,responses_api_models/vllm_model/configs/vllm_model.yaml]"
+```
+
+
+collect rollouts:
+
+ARC-AGI-1 example rollouts
+```bash
+ng_collect_rollouts +agent_name=arc_agi_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/example_1.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/example_1_rollouts.jsonl +limit=5 +num_repeats=null +num_samples_in_parallel=null
+```
+
+ARC-AGI-2 example rollouts:
+```bash
+ng_collect_rollouts +agent_name=arc_agi_2_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/example_2.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/example_2_rollouts.jsonl +limit=5 +num_repeats=null +num_samples_in_parallel=null
+```
+
+ARC-AGI-1 train set rollouts (400 problems):
+```bash
+ng_collect_rollouts +agent_name=arc_agi_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_1_training.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_1_training_rollouts.jsonl +limit=null +num_repeats=null +num_samples_in_parallel=null
+```
+
+ARC-AGI-1 eval set rollouts (400 problems):
+```bash
+ng_collect_rollouts +agent_name=arc_agi_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_1_evaluation.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_1_evaluation_rollouts.jsonl +limit=null +num_repeats=null +num_samples_in_parallel=null
+```
+
+ARC-AGI-2 train set rollouts (1000 problems):
+```bash
+ng_collect_rollouts +agent_name=arc_agi_2_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_2_training.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_2_training_rollouts.jsonl +limit=null +num_repeats=null +num_samples_in_parallel=null
+```
+
+ARC-AGI-2 eval set rollouts (120 problems):
+```bash
+ng_collect_rollouts +agent_name=arc_agi_2_simple_agent +input_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_2_evaluation.jsonl +output_jsonl_fpath=resources_servers/arc_agi/data/arc_agi_2_evaluation_rollouts.jsonl +limit=null +num_repeats=null +num_samples_in_parallel=null
+```
+
+run tests:
+```bash
+ng_test +entrypoint=resources_servers/arc_agi
+```

resources_servers/arc_agi/app.py

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+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import re
+from typing import List, Optional
+
+from fastapi import FastAPI
+
+from nemo_gym.base_resources_server import (
+    BaseResourcesServerConfig,
+    BaseRunRequest,
+    BaseVerifyRequest,
+    BaseVerifyResponse,
+    SimpleResourcesServer,
+)
+
+
+class ARCAGIResourcesServerConfig(BaseResourcesServerConfig):
+    pass
+
+
+class ARCAGIRunRequest(BaseRunRequest):
+    train: List[dict] = []
+    test_input: List[List[int]] = []
+    expected_output: List[List[int]] = []
+    task_id: Optional[str] = None
+
+
+class ARCAGIVerifyRequest(ARCAGIRunRequest, BaseVerifyRequest):
+    pass
+
+
+class ARCAGIVerifyResponse(BaseVerifyResponse):
+    expected_output: List[List[int]]
+    predicted_output: Optional[List[List[int]]] = None
+    extraction_successful: bool = False
+
+
+def _extract_assistant_text(body: BaseVerifyRequest) -> str:
+    texts = []
+    for output in body.response.output:
+        if getattr(output, "type", None) == "message" and getattr(output, "role", None) == "assistant":
+            content = getattr(output, "content", None)
+            if isinstance(content, list):
+                for part in content:
+                    text = getattr(part, "text", None)
+                    if isinstance(text, str):
+                        texts.append(text)
+            elif isinstance(content, str):
+                texts.append(content)
+    return "\n".join(texts).strip()
+
+
+def _parse_grid(text: str) -> Optional[List[List[int]]]:
+    """expects format: \\boxed{[[1,2,3],[4,5,6]]}"""
+    boxed_pattern = r"\\boxed\{(\[\s*\[[\d\s,\[\]]+\]\s*\])\}"
+    boxed_matches = re.findall(boxed_pattern, text, re.DOTALL)
+
+    if not boxed_matches:
+        boxed_matches = re.findall(r"\[\s*\[[\d\s,\[\]]+\]\s*\]", text, re.DOTALL)
+
+    for match in boxed_matches:
+        try:
+            cleaned = re.sub(r"\s+", "", match)
+            grid = json.loads(cleaned)
+
+            if (
+                isinstance(grid, list)
+                and all(isinstance(row, list) and all(isinstance(cell, int) for cell in row) for row in grid)
+                and len(grid) > 0
+                and len(grid[0]) > 0
+            ):
+                return grid
+        except (json.JSONDecodeError, IndexError, TypeError):
+            continue
+
+    return None
+
+
+class ARCAGIResourcesServer(SimpleResourcesServer):
+    config: ARCAGIResourcesServerConfig
+
+    def setup_webserver(self) -> FastAPI:
+        app = super().setup_webserver()
+        return app
+
+    async def verify(self, body: ARCAGIVerifyRequest) -> ARCAGIVerifyResponse:
+        assistant_text = _extract_assistant_text(body)
+        predicted_grid = _parse_grid(assistant_text)
+
+        extraction_successful = predicted_grid is not None
+        reward = 1.0 if extraction_successful and predicted_grid == body.expected_output else 0.0
+
+        return ARCAGIVerifyResponse(
+            **body.model_dump(),
+            reward=reward,
+            predicted_output=predicted_grid,
+            extraction_successful=extraction_successful,
+        )
+
+
+if __name__ == "__main__":
+    ARCAGIResourcesServer.run_webserver()

resources_servers/arc_agi/configs/arc_agi.yaml

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+arc_agi_resources_server:
+  resources_servers:
+    arc_agi:
+      entrypoint: app.py
+      domain: knowledge
+      verified: false
+arc_agi_simple_agent:
+  responses_api_agents:
+    simple_agent:
+      entrypoint: app.py
+      resources_server:
+        type: resources_servers
+        name: arc_agi_resources_server
+      model_server:
+        type: responses_api_models
+        name: policy_model
+      datasets:
+      - name: example
+        type: example
+        jsonl_fpath: resources_servers/arc_agi/data/example.jsonl
+      - name: training_1
+        type: validation
+        jsonl_fpath: resources_servers/arc_agi/data/arc_agi_1_training.jsonl
+        gitlab_identifier:
+          dataset_name: arc_agi
+          version: 0.0.1
+          artifact_fpath: arc_agi_1_training.jsonl
+        license: Apache 2.0
+      - name: evaluation_1
+        type: validation
+        jsonl_fpath: resources_servers/arc_agi/data/arc_agi_1_evaluation.jsonl
+        gitlab_identifier:
+          dataset_name: arc_agi
+          version: 0.0.1
+          artifact_fpath: arc_agi_1_evaluation.jsonl
+        license: Apache 2.0
+

resources_servers/arc_agi/configs/arc_agi_2.yaml

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+arc_agi_2:
+  resources_servers:
+    arc_agi:
+      entrypoint: app.py
+      domain: knowledge
+      verified: false
+arc_agi_2_simple_agent:
+  responses_api_agents:
+    simple_agent:
+      entrypoint: app.py
+      host: 127.0.0.1
+      port: 15215
+      resources_server:
+        type: resources_servers
+        name: arc_agi_2
+      model_server:
+        type: responses_api_models
+        name: policy_model
+      datasets:
+      - name: example_2
+        type: example
+        jsonl_fpath: resources_servers/arc_agi/data/example_2.jsonl
+      - name: training_2
+        type: validation
+        jsonl_fpath: resources_servers/arc_agi/data/arc_agi_2_training.jsonl
+      - name: evaluation_2
+        type: validation
+        jsonl_fpath: resources_servers/arc_agi/data/arc_agi_2_evaluation.jsonl

resources_servers/arc_agi/create_dataset.py

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+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+from pathlib import Path
+
+
+def format_grid(grid):
+    return "\n".join([" ".join(map(str, row)) for row in grid])
+
+
+def create_arc_prompt(task_data, task_id, version=1):
+    prompt = f"You are solving ARC-AGI{'-' + str(version) if version != 1 else ''} task {task_id}.\n\n"
+    prompt += "Here are the training examples that demonstrate the pattern:\n\n"
+
+    for i, example in enumerate(task_data["train"]):
+        prompt += f"Example {i + 1}:\n"
+        prompt += "Input:\n"
+        prompt += format_grid(example["input"])
+        prompt += "\n\nOutput:\n"
+        prompt += format_grid(example["output"])
+        prompt += "\n\n"
+
+    test_input = task_data["test"][0]["input"]
+    prompt += "Now solve this test case following the same pattern:\n"
+    prompt += "Test Input:\n"
+    prompt += format_grid(test_input)
+    prompt += (
+        "\n\nProvide your solution as a 2D array inside \\boxed{} in this exact format: \\boxed{[[row1],[row2],...]}"
+    )
+    prompt += "\nFor example: \\boxed{[[1,2,3],[4,5,6],[7,8,9]]}"
+
+    return prompt
+
+
+def create_dataset(version=1):
+    data_base = f"../../ARC-AGI{'-' + str(version) if version != 1 else ''}"
+    training_dir = Path(f"{data_base}/data/training")
+    evaluation_dir = Path(f"{data_base}/data/evaluation")
+
+    Path("data").mkdir(exist_ok=True)
+
+    training_dataset = []
+    print(f"Processing {len(list(training_dir.glob('*.json')))} training tasks...")  # 400 tasks
+
+    for task_file in sorted(training_dir.glob("*.json")):
+        task_id = task_file.stem
+
+        with open(task_file) as f:
+            task_data = json.load(f)
+
+        prompt = create_arc_prompt(task_data, task_id, version)
+        expected_output = task_data["test"][0]["output"]
+        test_input = task_data["test"][0]["input"]
+
+        entry = {
+            "responses_create_params": {"input": [{"role": "user", "content": prompt}]},
+            "train": task_data["train"],
+            "test_input": test_input,
+            "expected_output": expected_output,
+            "task_id": task_id,
+        }
+
+        training_dataset.append(entry)
+
+    training_output_file = Path(f"data/arc_agi_{version}_training.jsonl")
+    with open(training_output_file, "w") as f:
+        for entry in training_dataset:
+            f.write(json.dumps(entry) + "\n")
+
+    print(f"Created training dataset with {len(training_dataset)} tasks at {training_output_file}")
+
+    evaluation_dataset = []
+    print(f"Processing {len(list(evaluation_dir.glob('*.json')))} evaluation tasks...")  # 400 tasks
+
+    for task_file in sorted(evaluation_dir.glob("*.json")):
+        task_id = task_file.stem
+
+        with open(task_file) as f:
+            task_data = json.load(f)
+
+        prompt = create_arc_prompt(task_data, task_id, version)
+        expected_output = task_data["test"][0]["output"]
+        test_input = task_data["test"][0]["input"]
+
+        entry = {
+            "responses_create_params": {"input": [{"role": "user", "content": prompt}]},
+            "train": task_data["train"],
+            "test_input": test_input,
+            "expected_output": expected_output,
+            "task_id": task_id,
+        }
+
+        evaluation_dataset.append(entry)
+
+    evaluation_output_file = Path(f"data/arc_agi_{version}_evaluation.jsonl")
+    with open(evaluation_output_file, "w") as f:
+        for entry in evaluation_dataset:
+            f.write(json.dumps(entry) + "\n")
+
+    print(f"Created evaluation dataset with {len(evaluation_dataset)} tasks at {evaluation_output_file}")
+
+    example_output_file = Path(f"data/example_{version}.jsonl")
+    with open(example_output_file, "w") as f:
+        for entry in evaluation_dataset[:5]:
+            f.write(json.dumps(entry) + "\n")
+
+    print(f"Created example dataset with 5 tasks at {example_output_file}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Create ARC-AGI dataset")
+    parser.add_argument("--version", type=int, default=1, choices=[1, 2], help="ARC-AGI version (1 or 2)")
+    args = parser.parse_args()
+
+    create_dataset(version=args.version)