diff --git a/environments/eval_environments/super_gpqa_eval.py b/environments/eval_environments/super_gpqa_eval.py
new file mode 100644
index 000000000..1ccbb4af0
--- /dev/null
+++ b/environments/eval_environments/super_gpqa_eval.py
@@ -0,0 +1,712 @@
+"""
+SuperGPQA Evaluation Environment for Atropos
+
+This environment evaluates models on the SuperGPQA benchmark.
+
+Dataset: m-a-p/SuperGPQA
+Paper: https://www.arxiv.org/pdf/2502.14739
+
+SuperGPQA is a comprehensive benchmark designed to evaluate the
+knowledge and reasoning abilities of Large Language Models (LLMs)
+across 285 graduate-level disciplines.
+It features at least 50 questions per discipline,
+covering a broad spectrum of graduate-level topics.
+"""
+
+import asyncio
+import os
+import random
+import re
+import time
+from string import ascii_uppercase
+from typing import Dict, List, Optional, Tuple
+
+from datasets import load_dataset
+from eval_helpers import (
+    create_system_content,
+    extract_letter_from_answer_tag,
+    get_default_thinking_prompt,
+)
+from pydantic import Field
+from tqdm.asyncio import tqdm_asyncio
+
+from atroposlib.envs.base import (
+    APIServerConfig,
+    BaseEnv,
+    BaseEnvConfig,
+    EvalHandlingEnum,
+)
+
+SUPER_GPQA_ZERO_SHOT_PROMPT = """Answer the following multiple-choice question. There is only one correct answer.
+Provide your final answer within <answer></answer> tags, containing only the letter (A, B, C, D, E, F, G, H, I, or J.).
+
+Example format:
+<answer>A</answer>
+
+Question: {Question}"""
+
+
+class SuperGPQAEvalConfig(BaseEnvConfig):
+    """Configuration for SuperGPQA eval environment"""
+
+    custom_system_prompt: Optional[str] = Field(
+        default=None,
+        description="Custom system prompt to append after thinking prompt (if thinking_mode) or use directly.",
+    )
+
+    custom_thinking_prompt: Optional[str] = Field(
+        default=None,
+        description="Custom thinking prompt. If None, uses the default thinking prompt.",
+    )
+
+    thinking_mode: bool = Field(
+        default=False,
+        description="Whether to enable thinking mode with <think></think> tags.",
+    )
+
+    dataset_name: str = Field(
+        default="m-a-p/SuperGPQA",
+        description="HuggingFace dataset for SuperGPQA.",
+    )
+
+    eval_split: str = Field(
+        default="train",
+        description="Dataset split to use for evaluation (SuperGPQA has a single split 'train' used for evaluation).",
+    )
+
+    eval_max_tokens: int = Field(
+        default=32000,
+        description="Maximum tokens for reasoning models.",
+    )
+
+    min_response_length: int = Field(
+        default=1,
+        ge=1,
+        description="Min length for a valid response.",
+    )
+
+    # all main results in supergpqa use temperature=0
+    eval_temperature: float = Field(
+        default=0,
+        description="Temperature for evaluation (0.0 for deterministic).",
+    )
+
+    max_retries: int = Field(
+        default=3,
+        ge=1,
+        description="Maximum retries for failed API calls.",
+    )
+
+    retry_delay: float = Field(
+        default=1.0,
+        ge=0.0,
+        description="Delay between retry attempts in seconds.",
+    )
+
+    full_debug: bool = Field(
+        default=False,
+        description="Enable verbose debug logging.",
+    )
+
+    shuffle_seed: Optional[int] = Field(
+        default=42,
+        description="Seed for shuffling answer positions. Set to None for random shuffling each run.",
+    )
+
+
+class SuperGPQAEvalEnv(BaseEnv):
+    """
+    SuperGPQA eval environment for Atropos, supporting reasoning/instruct models via a 0-shot prompt.
+
+    Pipeline:
+    - Load SuperGPQA from HuggingFace
+    - Use 0-shot prompts from the SuperGPQA paper
+    - Extract answer choice via regex
+    """
+
+    name = "supergpqa_eval"
+    env_config_cls = SuperGPQAEvalConfig
+
+    def __init__(
+        self,
+        config: SuperGPQAEvalConfig,
+        server_configs: List[APIServerConfig],
+        slurm=True,
+        testing=False,
+    ):
+        super().__init__(config, server_configs, slurm, testing)
+        self.config: SuperGPQAEvalConfig = config
+
+        self.eval_metrics = []
+        if self.config.shuffle_seed is not None:
+            self.shuffle_rng = random.Random(self.config.shuffle_seed)
+        else:
+            self.shuffle_rng = random.Random()
+
+        self._think_pattern = re.compile(r"<think>")
+        self._think_close_pattern = re.compile(r"</think>")
+        self._think_content_pattern = re.compile(r"</think>\s*(.*)", re.DOTALL)
+        self._thinking_extract_pattern = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+        self._answer_pattern = re.compile(
+            r"<answer>(.*?)</answer>", re.DOTALL | re.IGNORECASE
+        )
+
+        # Build fallback answer extraction patterns
+        self._build_extraction_patterns()
+
+    def _get_thinking_prompt(self) -> str:
+        """Get thinking system prompt."""
+        return get_default_thinking_prompt(self.config.custom_thinking_prompt)
+
+    def _create_system_content(self) -> Optional[str]:
+        """Create system message content based on thinking mode."""
+        return create_system_content(
+            self.config.thinking_mode,
+            self.config.custom_thinking_prompt,
+            self.config.custom_system_prompt,
+        )
+
+    def _build_extraction_patterns(self):
+        """Build regex patterns for extracting answer letters from model responses."""
+        letters = "ABCDEFGHIJ"
+        letter_pattern = rf"([{letters}]|\([{letters}]\))"
+
+        # Patterns ordered by priority (most specific first)
+        self._pattern_final_answer_hope = re.compile(
+            rf"(?i:final\s+answer\s+is)\s*:?\s*{letter_pattern}\.?\s*I\s*hope",
+            re.IGNORECASE,
+        )
+        self._pattern_final_answer_is = re.compile(
+            rf"(?i:final\s+answer).{{0,100}}?\s+is\s*:?\s*{letter_pattern}",
+            re.IGNORECASE | re.DOTALL,
+        )
+        self._pattern_the_answer_is = re.compile(
+            rf"(?i:the\s+answer\s+is)\s*:?\s*{letter_pattern}", re.IGNORECASE
+        )
+        self._pattern_answer_colon = re.compile(
+            rf"(?i:answer)\s*:\s*.{{0,50}}?{letter_pattern}", re.IGNORECASE | re.DOTALL
+        )
+        self._pattern_answer_space = re.compile(
+            rf"(?i:answer)\s+{letter_pattern}", re.IGNORECASE
+        )
+        self._pattern_start = re.compile(
+            rf"^\s*\**{letter_pattern}\**[\s\.\)\:]", re.IGNORECASE
+        )
+        self._pattern_line_start = re.compile(
+            rf"\n\s*\**{letter_pattern}\**[\s\.\)\:]", re.IGNORECASE
+        )
+        self._pattern_standalone = re.compile(rf"\b{letter_pattern}\b", re.IGNORECASE)
+
+        self._extraction_patterns = [
+            (0, self._pattern_final_answer_hope, "final_answer_hope"),
+            (50, self._pattern_final_answer_is, "final_answer_is"),
+            (75, self._pattern_the_answer_is, "the_answer_is"),
+            (100, self._pattern_answer_colon, "answer_colon"),
+            (150, self._pattern_answer_space, "answer_space"),
+            (200, self._pattern_start, "start"),
+            (210, self._pattern_line_start, "line_start"),
+            (250, self._pattern_standalone, "standalone"),
+        ]
+
+    @classmethod
+    def config_init(cls) -> Tuple[SuperGPQAEvalConfig, List[APIServerConfig]]:
+        """Initialize default configuration for the environment."""
+        env_config = SuperGPQAEvalConfig(
+            tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B",
+            group_size=1,
+            use_wandb=True,
+            max_num_workers_per_node=128,
+            rollout_server_url="http://localhost:8000",
+            total_steps=1,
+            batch_size=1,
+            steps_per_eval=1,
+            inference_weight=1.0,
+            wandb_name="super_gpqa_eval",
+            eval_handling=EvalHandlingEnum.STOP_TRAIN,
+            max_eval_workers=256,
+            max_num_workers=1024,
+            dataset_name="m-a-p/SuperGPQA",
+            eval_temperature=0,
+            eval_max_tokens=0,  # this uses the default model max
+            thinking_mode=False,
+        )
+
+        server_configs = [
+            APIServerConfig(
+                model_name="Hermes-3-Llama-3.1-8B",
+                base_url="http://localhost:9000/v1",
+                api_key=os.getenv("OPENAI_API_KEY", "none"),
+                num_max_requests_at_once=32,
+                num_requests_for_eval=1024,
+            ),
+        ]
+
+        return env_config, server_configs
+
+    async def setup(self) -> None:
+        """Load SuperGPQA dataset and process all points to create prompts for rollout generation."""
+        print("\nSuperGPQA Evaluation Setup:")
+        print("=" * 20 + "DATASET DETAILS" + "=" * 20)
+        print(f"  Dataset: {self.config.dataset_name}")
+        print(f"  Evaluation split: {self.config.eval_split}")
+        print("=" * 20 + "GENERATION DETAILS" + "=" * 20)
+        print(f"  Thinking mode: {self.config.thinking_mode}")
+        print(f"  Max tokens: {self.config.eval_max_tokens}")
+
+        try:
+            dataset = load_dataset(
+                self.config.dataset_name,
+                split=self.config.eval_split,
+            )
+            self.eval_data = list(dataset)
+            print(f"  Loaded {len(self.eval_data)} evaluation items")
+        except Exception as e:
+            print(f"Error loading dataset from HuggingFace: {e}")
+            raise
+
+        self.all_eval_items = []
+        for item in self.eval_data:
+            processed = self._process_super_gpqa_item(item)
+            self.all_eval_items.append(processed)
+
+        self.iter = 0
+
+    def _process_super_gpqa_item(self, item: Dict) -> Dict:
+        # check if shuffling is done per rollout or once per eval
+        """
+        Process a SuperGPQA item.
+        Shuffle answer positions to avoid reward hacking where the model
+        might learn positions rather than the correct option.
+        """
+        correct_answer = item["answer"]
+        all_answers = item["options"]
+        choices = all_answers.copy()
+        self.shuffle_rng.shuffle(choices)
+        gold_index = choices.index(correct_answer)
+
+        return {
+            "question": item["question"],
+            "choices": choices,
+            "gold_index": gold_index,
+            "gold_letter": ascii_uppercase[gold_index],
+            "subfield": item.get("subfield", "unknown"),
+            "original_item": item,
+        }
+
+    def _format_super_gpqa_prompt(self, question: str, choices: List[str]) -> str:
+        """
+        Format a SuperGPQA question, add answer choices to the 0-shot prompt.
+        """
+        return (
+            SUPER_GPQA_ZERO_SHOT_PROMPT.format(
+                Question=question.strip(),
+            )
+            + "\n\n"
+            + "\n".join(
+                [
+                    f"{ascii_uppercase[i]}) {choice.strip()}"
+                    for i, choice in enumerate(choices)
+                ]
+            )
+        )
+
+    def _validate_thinking_format(self, response: str) -> Tuple[bool, str]:
+        """Validate thinking format and extract content after </think> tags."""
+        if not self.config.thinking_mode:
+            return True, response
+
+        think_open_count = len(self._think_pattern.findall(response))
+        think_close_count = len(self._think_close_pattern.findall(response))
+
+        if think_open_count != 1 or think_close_count != 1:
+            return False, response
+
+        match = self._think_content_pattern.search(response)
+        if match:
+            return True, match.group(1).strip()
+        else:
+            return False, response
+
+    def _extract_thinking_content(self, response: str) -> Optional[str]:
+        """Extract the content inside <think></think> tags."""
+        match = self._thinking_extract_pattern.search(response)
+        if match:
+            return match.group(1).strip()
+        return None
+
+    def _extract_answer(
+        self, response: str, num_choices: int, choices: Optional[List[str]] = None
+    ) -> Tuple[Optional[str], str]:
+        """
+        Extract the answer letter from the model's response.
+
+        Primary method: Look for <answer></answer> tags, or match against choice texts.
+        Fallback: Use priority-ordered regex patterns.
+        """
+        if not response:
+            return None, "empty_response"
+
+        valid_letters = set(ascii_uppercase[:num_choices])
+
+        # PRIMARY: Try <answer></answer> tags first
+        # Also matches against choice texts if provided
+        letter, method = extract_letter_from_answer_tag(
+            response, valid_letters, debug=self.config.full_debug, choices=choices
+        )
+        if letter:
+            return letter, method
+
+        # FALLBACK: Try each pattern in priority order
+        for _, pattern, method_name in self._extraction_patterns:
+            matches = pattern.findall(response)
+            if matches:
+                match = (
+                    matches[-1]
+                    if method_name
+                    in [
+                        "final_answer_is",
+                        "the_answer_is",
+                        "answer_colon",
+                        "answer_space",
+                    ]
+                    else matches[0]
+                )
+                if isinstance(match, tuple):
+                    match = match[0]
+                letter = match.strip("()").upper()
+
+                if letter in valid_letters:
+                    if self.config.full_debug:
+                        print(
+                            f"    Extracted '{letter}' using fallback method '{method_name}'"
+                        )
+                    return letter, f"fallback_{method_name}"
+
+        for letter in reversed(list(valid_letters)):
+            if letter in response.upper():
+                if self.config.full_debug:
+                    print(
+                        f"    Extracted '{letter}' using fallback 'last_valid_letter'"
+                    )
+                return letter, "fallback_last_valid_letter"
+
+        return None, "no_match"
+
+    async def get_next_item(self):
+        """Get next item for training (not used in eval-only environment)."""
+        self.iter += 1
+        if self.all_eval_items:
+            item = self.all_eval_items[self.iter % len(self.all_eval_items)]
+            return item
+        return None
+
+    async def collect_trajectories(self, item):
+        """Collect trajectories (not used in eval-only environment)."""
+        return None, []
+
+    async def score(self, rollout_group_data):
+        """Score rollouts (not used in eval-only environment)."""
+        return None
+
+    async def rollout_and_score_eval(self, eval_item: Dict) -> Dict:
+        """Evaluate a single SuperGPQA question."""
+        try:
+            question = eval_item.get("question", "")
+            choices = eval_item.get("choices", [])
+            gold_letter = eval_item.get("gold_letter", "A")
+            subfield = eval_item.get("subfield", "unknown")
+
+            if not question or len(choices) < 2:
+                return {"is_correct": None, "sample": None}
+
+            formatted_prompt = self._format_super_gpqa_prompt(question, choices)
+
+            messages = []
+            system_content = self._create_system_content()
+            if system_content:
+                messages.append({"role": "system", "content": system_content})
+            messages.append({"role": "user", "content": formatted_prompt})
+
+            model_response = None
+            finish_reason = None
+
+            completion_kwargs = {
+                "messages": messages,
+                "n": 1,
+                "temperature": self.config.eval_temperature,
+                "split": "eval",
+            }
+            if (
+                self.config.eval_max_tokens > 0
+            ):  # 0 means "use model default", so we don't pass the parameter
+                completion_kwargs["max_tokens"] = self.config.eval_max_tokens
+
+            for attempt in range(self.config.max_retries):
+                try:
+                    completion = await self.server.chat_completion(**completion_kwargs)
+
+                    if completion.choices and completion.choices[0].message.content:
+                        model_response = completion.choices[0].message.content
+                        finish_reason = getattr(
+                            completion.choices[0], "finish_reason", None
+                        )
+
+                        if (
+                            len(model_response.strip())
+                            >= self.config.min_response_length
+                        ):
+                            break
+                        elif attempt < self.config.max_retries - 1:
+                            if self.config.full_debug:
+                                print("  Response too short, retrying...")
+                            await asyncio.sleep(self.config.retry_delay)
+
+                except Exception as e:
+                    # Always log API errors to help diagnose issues
+                    print(
+                        f"  API Error (attempt {attempt + 1}/{self.config.max_retries}): {type(e).__name__}: {e}"
+                    )
+                    if hasattr(e, "response"):
+                        try:
+                            print(
+                                f"    Response: {e.response.text[:500] if hasattr(e.response, 'text') else e.response}"
+                            )
+                        except Exception:
+                            pass
+                    if attempt < self.config.max_retries - 1:
+                        await asyncio.sleep(self.config.retry_delay)
+                    else:
+                        print(f"  Failed after {self.config.max_retries} attempts")
+                        return {"is_correct": None, "sample": None}
+
+            if not model_response:
+                return {"is_correct": None, "sample": None}
+
+            # Validate thinking format if enabled
+            format_valid, content_for_extraction = self._validate_thinking_format(
+                model_response
+            )
+
+            # Extract thinking content for logging
+            thinking_content = None
+            if self.config.thinking_mode:
+                thinking_content = self._extract_thinking_content(model_response)
+
+            extracted_answer, extraction_method = self._extract_answer(
+                content_for_extraction, num_choices=len(choices), choices=choices
+            )
+            is_correct = extracted_answer == gold_letter if extracted_answer else False
+
+            # Build sample record
+            sample = {
+                "question": question,
+                "choices": choices,
+                "gold_answer": gold_letter,
+                "model_response": model_response,
+                "extracted_answer": extracted_answer,
+                "extraction_method": extraction_method,
+                "is_correct": is_correct,
+                "subfield": subfield,
+                "finish_reason": finish_reason,
+                "response_length": len(model_response),
+                "thinking_mode": self.config.thinking_mode,
+                "format_valid": format_valid,
+            }
+
+            if self.config.thinking_mode:
+                sample["thinking_content"] = thinking_content
+                sample["response_after_think"] = (
+                    content_for_extraction if format_valid else None
+                )
+
+            if self.config.full_debug:
+                status = "✓" if is_correct else "✗"
+                print(
+                    f"  [{status}] {subfield}: gold={gold_letter}, extracted={extracted_answer}"
+                )
+
+            return {"is_correct": is_correct, "sample": sample}
+
+        except Exception as e:
+            if self.config.full_debug:
+                print(f"Error in rollout_and_score_eval: {e}")
+                import traceback
+
+                traceback.print_exc()
+            return {"is_correct": None, "sample": None}
+
+    async def evaluate(self, *args, **kwargs) -> None:
+        """Run SuperGPQA evaluation."""
+        start_time = time.time()
+
+        print(f"\n{'='*60}")
+        print("Starting SuperGPQA Evaluation:")
+        print(f"{'='*60}")
+        print(f"  Total questions: {len(self.all_eval_items)}")
+        print(f"  Max tokens (for reasoning): {self.config.eval_max_tokens}")
+        print(f"  Thinking mode: {self.config.thinking_mode}")
+        print(f"{'='*60}\n")
+
+        try:
+            eval_tasks = [
+                self.rollout_and_score_eval(item) for item in self.all_eval_items
+            ]
+            results = await tqdm_asyncio.gather(
+                *eval_tasks, desc="Evaluating SuperGPQA"
+            )
+
+            valid_results = [
+                r
+                for r in results
+                if r and r.get("sample") is not None and r.get("is_correct") is not None
+            ]
+
+            if not valid_results:
+                print("Warning: No valid evaluation results obtained")
+                return
+
+        except Exception as e:
+            print(f"Error during evaluation: {e}")
+            import traceback
+
+            traceback.print_exc()
+            return
+
+        end_time = time.time()
+
+        # Compute metrics
+        samples = [r["sample"] for r in valid_results]
+
+        # Overall accuracy
+        total_correct = sum(1 for r in valid_results if r["is_correct"])
+        total_count = len(valid_results)
+        overall_accuracy = total_correct / total_count if total_count > 0 else 0.0
+
+        # Per-subfield accuracy
+        subfield_results = {}
+        for sample in samples:
+            subfield = sample.get("subfield", "unknown")
+            if subfield not in subfield_results:
+                subfield_results[subfield] = {"correct": 0, "total": 0}
+            subfield_results[subfield]["total"] += 1
+            if sample["is_correct"]:
+                subfield_results[subfield]["correct"] += 1
+
+        # Extraction method statistics
+        extraction_methods = {}
+        for sample in samples:
+            method = sample.get("extraction_method", "unknown")
+            if method not in extraction_methods:
+                extraction_methods[method] = {"count": 0, "correct": 0}
+            extraction_methods[method]["count"] += 1
+            if sample["is_correct"]:
+                extraction_methods[method]["correct"] += 1
+
+        # Average response length
+        response_lengths = [s.get("response_length", 0) for s in samples]
+        avg_response_length = (
+            sum(response_lengths) / len(response_lengths) if response_lengths else 0
+        )
+
+        # Format compliance
+        format_compliant = sum(1 for s in samples if s.get("format_valid", True))
+        format_compliance_rate = format_compliant / len(samples) if samples else 0.0
+
+        # Thinking utilization
+        thinking_utilization = 0
+        if self.config.thinking_mode:
+            thinking_utilization = sum(1 for s in samples if s.get("thinking_content"))
+
+        # Build metrics dictionary
+        eval_metrics = {
+            "eval/overall_accuracy": overall_accuracy,
+            "eval/total_questions": total_count,
+            "eval/total_correct": total_correct,
+            "eval/evaluation_time_seconds": end_time - start_time,
+            "eval/avg_response_length": avg_response_length,
+            "eval/format_compliance_rate": format_compliance_rate,
+            "eval/thinking_mode_enabled": 1.0 if self.config.thinking_mode else 0.0,
+        }
+
+        if self.config.thinking_mode:
+            thinking_utilization_rate = (
+                thinking_utilization / len(samples) if samples else 0.0
+            )
+            eval_metrics["eval/thinking_utilization_rate"] = thinking_utilization_rate
+
+        # Add subfield metrics
+        for subfield, stats in subfield_results.items():
+            if stats["total"] > 0:
+                subfield_accuracy = stats["correct"] / stats["total"]
+                subfield_key = subfield.replace(" ", "_").replace("-", "_").lower()
+                eval_metrics[f"eval/subfield_{subfield_key}_accuracy"] = (
+                    subfield_accuracy
+                )
+
+        # Store metrics for wandb logging
+        self.eval_metrics = [(k, v) for k, v in eval_metrics.items()]
+
+        # Print summary
+        print(f"\n{'='*60}")
+        print("SuperGPQA Evaluation Results")
+        print(f"{'='*60}")
+        print(
+            f"Overall Accuracy: {overall_accuracy:.4f} ({total_correct}/{total_count})"
+        )
+        print(f"Evaluation Time: {end_time - start_time:.1f} seconds")
+        print(f"Avg Response Length: {avg_response_length:.0f} chars")
+        if self.config.thinking_mode:
+            print(f"Format Compliance: {format_compliance_rate:.4f}")
+            print(f"Thinking Utilization: {thinking_utilization}/{total_count}")
+
+        print("\nSubfield Breakdown:")
+        for subfield, stats in sorted(subfield_results.items()):
+            if stats["total"] > 0:
+                subfield_acc = stats["correct"] / stats["total"]
+                print(
+                    f"  {subfield}: {subfield_acc:.4f} ({stats['correct']}/{stats['total']})"
+                )
+
+        print("\nExtraction Method Statistics:")
+        for method, stats in sorted(
+            extraction_methods.items(), key=lambda x: -x[1]["count"]
+        ):
+            if stats["count"] > 0:
+                method_acc = stats["correct"] / stats["count"]
+                print(f"  {method}: {stats['count']} uses, {method_acc:.4f} accuracy")
+
+        print(f"{'='*60}\n")
+
+        try:
+            await self.evaluate_log(
+                metrics=eval_metrics,
+                start_time=start_time,
+                end_time=end_time,
+                samples=samples,
+                generation_parameters={
+                    "temperature": self.config.eval_temperature,
+                    "max_tokens": self.config.eval_max_tokens,
+                    "thinking_mode": self.config.thinking_mode,
+                },
+            )
+        except Exception as e:
+            print(f"Error logging evaluation results: {e}")
+
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        """Log metrics to wandb."""
+        if wandb_metrics is None:
+            wandb_metrics = {}
+
+        for metric_name, metric_value in self.eval_metrics:
+            wandb_metrics[metric_name] = metric_value
+        self.eval_metrics = []
+
+        wandb_metrics["config/thinking_mode"] = (
+            1.0 if self.config.thinking_mode else 0.0
+        )
+        wandb_metrics["config/eval_max_tokens"] = self.config.eval_max_tokens
+        await super().wandb_log(wandb_metrics)
+
+
+if __name__ == "__main__":
+    SuperGPQAEvalEnv.cli()