diff --git a/environments/__init__.py b/environments/__init__.py
index f0c959caeda9..d1a40eb635f1 100644
--- a/environments/__init__.py
+++ b/environments/__init__.py
@@ -13,6 +13,7 @@
 Concrete environments:
     - terminal_test_env/: Simple file-creation tasks for testing the stack
     - hermes_swe_env/: SWE-bench style tasks with Modal sandboxes
+    - endless_terminals/: Terminal tasks from HuggingFace dataset with Apptainer containers
 
 Benchmarks (eval-only):
     - benchmarks/terminalbench_2/: Terminal-Bench 2.0 evaluation
diff --git a/environments/endless_terminals/__init__.py b/environments/endless_terminals/__init__.py
new file mode 100644
index 000000000000..f70584951247
--- /dev/null
+++ b/environments/endless_terminals/__init__.py
@@ -0,0 +1,5 @@
+"""Endless Terminals Environment - Terminal task training from HuggingFace dataset."""
+
+from .endless_terminals_env import EndlessTerminalsEnv, EndlessTerminalsEnvConfig
+
+__all__ = ["EndlessTerminalsEnv", "EndlessTerminalsEnvConfig"]
diff --git a/environments/endless_terminals/default.yaml b/environments/endless_terminals/default.yaml
new file mode 100644
index 000000000000..4ae8da550c6b
--- /dev/null
+++ b/environments/endless_terminals/default.yaml
@@ -0,0 +1,69 @@
+# Endless Terminals Environment -- Default Configuration
+#
+# Trains agents on terminal tasks from the Endless Terminals HuggingFace dataset.
+# Uses hermes-agent backends (modal/docker/local) with per-task Docker images.
+# Tests run in the same sandbox the agent used (no separate containers needed).
+#
+# Dataset: https://huggingface.co/datasets/obiwan96/endless-terminals-train
+#
+# Prerequisites:
+#   1. Download dataset: huggingface-cli download obiwan96/endless-terminals-train \
+#        --repo-type dataset --local-dir ~/endless-terminals-data \
+#        --local-dir-use-symlinks False
+#   2. Set TASKS_BASE_DIR environment variable or configure tasks_base_dir below
+#   3. For modal backend: Configure Modal CLI (modal token set)
+#   4. For docker backend: Install Docker
+#
+# Usage:
+#   python environments/endless_terminals/endless_terminals_env.py process \
+#       --config environments/endless_terminals/default.yaml
+
+env:
+  # Toolsets
+  enabled_toolsets: ["terminal", "file"]
+
+  # Agent configuration
+  max_agent_turns: 32
+  max_token_length: 4096
+  agent_temperature: 1.0
+
+  # Terminal backend
+  terminal_backend: "local"  # Change to "modal" or "docker" for cloud isolation
+
+  # Dataset settings
+  use_dataset: true
+  dataset_name: "obiwan96/endless-terminals"
+  dataset_split: "train"
+  dataset_cache_dir: "~/.cache/huggingface/datasets"
+  tasks_base_dir: ""  # Set to directory containing task_* folders (e.g., ~/endless-terminals-data)
+
+  # Test execution
+  test_timeout_s: 60
+
+  # Training configuration
+  group_size: 8
+  total_steps: 10000
+  steps_per_eval: 500
+
+  num_eval_tasks: 10
+  eval_split_ratio: 0.1
+
+  # Logging
+  use_wandb: true
+  wandb_name: "endless-terminals"
+
+  # System prompt
+  system_prompt: >
+    You are a skilled Linux system administrator and programmer.
+    You have access to a terminal and file tools to complete system administration
+    and programming tasks. Use the tools effectively to solve the given task,
+    and verify your solution works correctly before finishing.
+
+openai:
+  base_url: "https://openrouter.ai/api/v1"
+  model_name: "anthropic/claude-sonnet-4.5"
+  server_type: "openai"
+  api_key: ""  # Loaded from OPENROUTER_API_KEY env var
+  health_check: false
+  timeout: 30  # 30 second timeout per request
+  max_retries: 2  # Only retry twice
\ No newline at end of file
diff --git a/environments/endless_terminals/endless_terminals_env.py b/environments/endless_terminals/endless_terminals_env.py
new file mode 100644
index 000000000000..ac8bfbc801c5
--- /dev/null
+++ b/environments/endless_terminals/endless_terminals_env.py
@@ -0,0 +1,921 @@
+"""
+Endless Terminals Environment for Hermes-Agent + Atropos RL.
+
+Loads pre-generated terminal tasks from HuggingFace dataset and scores
+agent performance using test execution in the agent's sandbox.
+
+Uses hermes-agent backends (modal, docker, local) with per-task Docker images
+extracted from container.def files. Tests run in the same sandbox the agent
+used, following the Terminal Bench 2 pattern.
+
+Dataset: https://huggingface.co/datasets/obiwan96/endless-terminals-train
+
+Run:
+  python environments/endless_terminals/endless_terminals_env.py process \
+    --config environments/endless_terminals/default.yaml
+"""
+
+import asyncio
+import logging
+import os
+import random
+import re
+import sys
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from pydantic import Field
+
+# Ensure hermes-agent root is on path
+_repo_root = Path(__file__).resolve().parent.parent.parent
+if str(_repo_root) not in sys.path:
+    sys.path.insert(0, str(_repo_root))
+
+from atroposlib.envs.base import ScoredDataGroup, ScoredDataItem
+from atroposlib.type_definitions import Item
+
+from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
+from environments.agent_loop import AgentResult
+from environments.tool_context import ToolContext
+from tools.terminal_tool import (
+    register_task_env_overrides,
+    clear_task_env_overrides,
+    cleanup_vm,
+)
+
+logger = logging.getLogger(__name__)
+
+# Add endless-terminals to path for imports
+ENDLESS_TERMINALS_PATH = os.getenv(
+    "ENDLESS_TERMINALS_PATH",
+    str(Path.home() / "Desktop" / "Projects" / "endless-terminals")
+)
+sys.path.insert(0, ENDLESS_TERMINALS_PATH)
+
+
+class EndlessTerminalsEnvConfig(HermesAgentEnvConfig):
+    """Configuration for Endless Terminals environment."""
+
+    # Dataset settings
+    use_dataset: bool = Field(
+        default=True,
+        description="Load tasks from HuggingFace dataset (recommended). If False, generate procedurally."
+    )
+    dataset_name: str = Field(
+        default="obiwan96/endless-terminals-train",
+        description="HuggingFace dataset name"
+    )
+    dataset_split: str = Field(
+        default="train",
+        description="Dataset split to use"
+    )
+    dataset_cache_dir: str = Field(
+        default="~/.cache/huggingface/datasets",
+        description="HuggingFace datasets cache directory"
+    )
+    tasks_base_dir: str = Field(
+        default="",
+        description="Base directory containing task_* folders. If empty, uses paths from dataset."
+    )
+
+    # Test execution
+    test_timeout_s: int = Field(default=60, description="Test execution timeout (seconds)")
+
+    # Docker image fallback
+    default_docker_image: str = Field(
+        default="ubuntu:22.04",
+        description="Default Docker image if container.def parsing fails"
+    )
+
+    # Agent defaults
+    max_agent_turns: int = Field(default=32, description="Max turns for agent (increased for long traces)")
+
+    # Evaluation settings
+    num_eval_tasks: int = Field(
+        default=10,
+        description="Number of tasks to run during periodic evaluation"
+    )
+    eval_split_ratio: float = Field(
+        default=0.1,
+        description="Fraction of dataset to hold out for evaluation (0.0-1.0)"
+    )
+
+
+class EndlessTerminalsEnv(HermesAgentBaseEnv):
+    """
+    Endless Terminals environment using pre-generated HuggingFace dataset.
+
+    Loads terminal tasks from dataset, runs agent with terminal tools,
+    and scores by executing tests in the agent's sandbox using ToolContext.
+    """
+
+    name = "endless_terminals_env"
+    env_config_cls = EndlessTerminalsEnvConfig
+
+    @classmethod
+    def config_init(cls) -> Tuple[EndlessTerminalsEnvConfig, List["APIServerConfig"]]:
+        """
+        Default configuration for Endless Terminals environment.
+
+        This is used when no config file is provided, but note that when using
+        --config, the YAML is loaded differently and this may not be called.
+        """
+        from atroposlib.envs.server_handling.server_manager import APIServerConfig
+
+        env_config = EndlessTerminalsEnvConfig(
+            enabled_toolsets=["terminal", "file"],
+            max_agent_turns=32,
+            terminal_backend="local",
+            use_dataset=True,
+            tasks_base_dir="",
+            group_size=1,
+            total_steps=1,
+            use_wandb=False,
+        )
+
+        server_configs = [
+            APIServerConfig(
+                base_url="https://openrouter.ai/api/v1",
+                model_name="anthropic/claude-sonnet-4.5",
+                server_type="openai",
+                api_key=os.getenv("OPENROUTER_API_KEY", ""),
+                health_check=False,
+            )
+        ]
+
+        return env_config, server_configs
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._dataset = None
+        self._train_dataset = None
+        self._eval_dataset = None
+        self._dataset_indices = []
+        self._current_index = 0
+
+        # Metrics tracking for wandb - single buffer with dicts
+        self._metrics_buffer = []
+
+        # Debug: check server config
+        if hasattr(self, 'server') and hasattr(self.server, 'servers'):
+            for i, srv in enumerate(self.server.servers):
+                logger.debug(f"Server {i}: model_name={getattr(srv.config, 'model_name', 'NONE')}")
+
+    async def setup(self):
+        """Load dataset from HuggingFace or local directory."""
+        if not self.config.use_dataset:
+            logger.info("Using procedural task generation (not implemented yet)")
+            return
+
+        # If tasks_base_dir is set, load from local directory instead of HuggingFace
+        if self.config.tasks_base_dir:
+            tasks_base = Path(os.path.expanduser(self.config.tasks_base_dir))
+
+            # Resolve to absolute path if relative
+            if not tasks_base.is_absolute():
+                tasks_base = Path.cwd() / tasks_base
+
+            tasks_base = tasks_base.resolve()
+
+            if not tasks_base.exists():
+                raise RuntimeError(f"tasks_base_dir not found: {tasks_base}")
+
+            logger.info(f"Loading tasks from local directory: {tasks_base}")
+
+            # Find all task_* directories
+            task_dirs = sorted(tasks_base.glob("task_*"))
+            logger.info(f"Found {len(task_dirs)} task directories")
+
+            if not task_dirs:
+                # Debug: show what's actually in the directory
+                all_items = list(tasks_base.iterdir())
+                logger.warning(f"Directory contains {len(all_items)} items:")
+                for item in all_items[:10]:
+                    logger.warning(f"  - {item.name} ({'dir' if item.is_dir() else 'file'})")
+                raise RuntimeError(f"No task_* directories found in {tasks_base}")
+
+            # Create fake dataset items (just the directory paths)
+            self._dataset = [
+                {
+                    "description": f"Task from {task_dir.name}",
+                    "extra_info": {"task_dir": str(task_dir)},
+                }
+                for task_dir in task_dirs
+            ]
+
+            logger.info(f"Loaded {len(self._dataset)} tasks from local directory")
+
+            self._split_dataset()
+            return
+
+        # Otherwise, load from HuggingFace
+        logger.info(f"Loading dataset from HuggingFace: {self.config.dataset_name}")
+
+        try:
+            from datasets import load_dataset
+
+            self._dataset = await asyncio.get_event_loop().run_in_executor(
+                None,
+                lambda: load_dataset(
+                    self.config.dataset_name,
+                    split=self.config.dataset_split,
+                    cache_dir=os.path.expanduser(self.config.dataset_cache_dir)
+                )
+            )
+
+            logger.info(f"Loaded {len(self._dataset)} tasks from HuggingFace")
+
+            self._split_dataset()
+
+        except Exception as e:
+            logger.error(f"ERROR loading dataset: {e}")
+            raise
+
+    def _split_dataset(self):
+        """Split dataset into train and eval sets based on eval_split_ratio."""
+        if self._dataset is None or len(self._dataset) == 0:
+            raise RuntimeError("Cannot split empty dataset")
+
+        total_size = len(self._dataset)
+        eval_size = int(total_size * self.config.eval_split_ratio)
+        train_size = total_size - eval_size
+
+        all_indices = list(range(total_size))
+        random.shuffle(all_indices)
+
+        train_indices = all_indices[:train_size]
+        eval_indices = all_indices[train_size:]
+
+        if isinstance(self._dataset, list):
+            self._train_dataset = [self._dataset[i] for i in train_indices]
+            self._eval_dataset = [self._dataset[i] for i in eval_indices]
+        else:
+            self._train_dataset = self._dataset.select(train_indices)
+            self._eval_dataset = self._dataset.select(eval_indices)
+
+        self._dataset_indices = list(range(len(self._train_dataset)))
+        random.shuffle(self._dataset_indices)
+        self._current_index = 0
+
+        logger.info(
+            f"Split dataset: {len(self._train_dataset)} train, "
+            f"{len(self._eval_dataset)} eval "
+            f"(ratio={self.config.eval_split_ratio:.1%})"
+        )
+
+    async def get_next_item(self) -> Item:
+        """Sample next task from training dataset."""
+        if self._train_dataset is None:
+            raise RuntimeError("Dataset not loaded. Call setup() first.")
+
+        # Get next task (with wraparound)
+        idx = self._dataset_indices[self._current_index]
+        task = self._train_dataset[idx]
+
+        # Advance to next task
+        self._current_index += 1
+        if self._current_index >= len(self._dataset_indices):
+            # Reshuffle for next epoch
+            random.shuffle(self._dataset_indices)
+            self._current_index = 0
+            logger.info("Reshuffled dataset (completed one epoch)")
+
+        # Extract task directory path
+        task_dir = task.get("extra_info", {}).get("task_dir")
+        if not task_dir:
+            task_dir = task.get("reward_spec", {}).get("ground_truth")
+
+        # Resolve task directory path
+        if task_dir:
+            task_dir_path = Path(task_dir)
+            # If tasks_base_dir is configured and path doesn't exist, reconstruct it
+            if self.config.tasks_base_dir and not task_dir_path.exists():
+                original_path = Path(task_dir)
+                task_name = original_path.name
+                task_dir_path = Path(os.path.expanduser(self.config.tasks_base_dir)) / task_name
+        else:
+            logger.error("No task directory path found in dataset item")
+            return await self.get_next_item()
+
+        # Verify directory exists
+        if not task_dir_path.exists():
+            logger.warning(f"Task dir not found: {task_dir_path}")
+            logger.warning("Hint: Set tasks_base_dir to directory containing task_* folders")
+            return await self.get_next_item()  # Try next task
+
+        # Look for test file in tests/ subdirectory first, then at root
+        final_test = task_dir_path / "tests" / "test_final_state.py"
+        if not final_test.exists():
+            final_test = task_dir_path / "test_final_state.py"
+
+        # Verify test file exists
+        if not final_test.exists():
+            logger.warning(f"Missing test file in {task_dir_path} (checked tests/ and root)")
+            return await self.get_next_item()
+
+        # Parse container.def to extract Docker image
+        # Check environment/ subdirectory first, then root
+        container_def = task_dir_path / "environment" / "container.def"
+        if not container_def.exists():
+            container_def = task_dir_path / "container.def"
+        docker_image = self._parse_docker_image_from_def(container_def)
+
+        # Try to load description from instruction.md or task.json
+        description = task.get("description", "")
+
+        # First try instruction.md
+        instruction_md = task_dir_path / "instruction.md"
+        if not description and instruction_md.exists():
+            try:
+                description = instruction_md.read_text().strip()
+            except Exception as e:
+                logger.warning(f"Failed to load instruction.md for {task_dir_path.name}: {e}")
+
+        # Fallback to task.json in environment/
+        if not description:
+            task_json = task_dir_path / "environment" / "task.json"
+            if task_json.exists():
+                try:
+                    import json
+                    task_data = json.loads(task_json.read_text())
+                    description = task_data.get("description", "") or task_data.get("instruction", "")
+                except Exception as e:
+                    logger.warning(f"Failed to load task.json for {task_dir_path.name}: {e}")
+
+        if not description:
+            description = f"Complete the task in {task_dir_path.name}"
+
+        return {
+            "task_id": f"{task_dir_path.name}",
+            "task_name": task_dir_path.name,
+            "description": description,
+            "task_dir": str(task_dir_path),
+            "final_test": str(final_test),
+            "docker_image": docker_image,
+            "dataset_index": idx,
+        }
+
+    def format_prompt(self, item: Item) -> str:
+        """Return the task description for the agent."""
+        return str(item.get("description", ""))
+
+    def _parse_docker_image_from_def(self, container_def_path: Path) -> str:
+        """
+        Parse container.def file to extract the Docker base image.
+
+        Apptainer definition files typically look like:
+            Bootstrap: docker
+            From: ubuntu:22.04
+
+        Returns the image from the "From:" line, or falls back to default.
+        """
+        if not container_def_path.exists():
+            logger.warning(f"container.def not found at {container_def_path}, using default image")
+            return self.config.default_docker_image
+
+        try:
+            content = container_def_path.read_text()
+            # Look for "From: <image>" line (case-insensitive)
+            match = re.search(r'^From:\s*(.+)$', content, re.MULTILINE | re.IGNORECASE)
+            if match:
+                image = match.group(1).strip()
+                logger.info(f"Extracted Docker image from container.def: {image}")
+                return image
+        except Exception as e:
+            logger.warning(f"Failed to parse {container_def_path}: {e}")
+
+        logger.warning(f"Could not extract image from {container_def_path}, using default")
+        return self.config.default_docker_image
+
+    async def collect_trajectory(
+        self, item: Item
+    ) -> Tuple[Optional[ScoredDataItem], List[Item]]:
+        """
+        Override to register per-task Docker image before running the agent.
+
+        Follows Terminal Bench 2 pattern: register_task_env_overrides() tells
+        the hermes-agent terminal backend to use a specific Docker image for
+        this task_id.
+
+        This is a copy of HermesAgentBaseEnv.collect_trajectory with Docker
+        image registration added after task_id generation.
+        """
+        import uuid
+        from environments.agent_loop import HermesAgentLoop
+
+        task_id = str(uuid.uuid4())
+        task_name = item.get("task_name", "unknown")
+        docker_image = item.get("docker_image", self.config.default_docker_image)
+
+        logger.debug(f"collect_trajectory START for {task_name}")
+
+        # Register Docker image override for this task_id
+        logger.debug(f"Registering Docker image: {docker_image}")
+        register_task_env_overrides(task_id, {"modal_image": docker_image})
+        logger.info(
+            f"Task {task_name}: registered Docker image {docker_image} for task_id {task_id[:8]}"
+        )
+        logger.debug("Docker image registered")
+
+        try:
+            # Get group-level tools (resolved once in collect_trajectories)
+            logger.debug("Resolving tools...")
+            if self._current_group_tools is None:
+                tools, valid_names = self._resolve_tools_for_group()
+            else:
+                tools, valid_names = self._current_group_tools
+            logger.debug(f"Tools resolved: {len(tools)} tools")
+
+            # Build initial messages
+            logger.debug("Building initial messages...")
+            messages: List[Dict[str, Any]] = []
+            if self.config.system_prompt:
+                messages.append({"role": "system", "content": self.config.system_prompt})
+            messages.append({"role": "user", "content": self.format_prompt(item)})
+            logger.debug("Messages built, starting agent loop...")
+
+            # Run the agent loop
+            result: AgentResult
+            managed_state: Optional[Dict[str, Any]] = None
+
+            if self._use_managed_server():
+                # Phase 2: ManagedServer with parser
+                from environments.tool_call_parsers import get_parser
+                try:
+                    tc_parser = get_parser(self.config.tool_call_parser)
+                except KeyError:
+                    logger.warning(
+                        "Tool call parser '%s' not found, falling back to 'hermes'",
+                        self.config.tool_call_parser,
+                    )
+                    tc_parser = get_parser("hermes")
+
+                try:
+                    async with self.server.managed_server(
+                        tokenizer=self.tokenizer,
+                        tool_call_parser=tc_parser,
+                    ) as managed:
+                        agent = HermesAgentLoop(
+                            server=managed,
+                            tool_schemas=tools,
+                            valid_tool_names=valid_names,
+                            max_turns=self.config.max_agent_turns,
+                            task_id=task_id,
+                            temperature=self.config.agent_temperature,
+                            max_tokens=self.config.max_token_length,
+                            extra_body=self.config.extra_body,
+                        )
+                        result = await agent.run(messages)
+
+                        # Get state directly from managed server while still in context
+                        managed_state = managed.get_state()
+                except NotImplementedError:
+                    # DummyManagedServer not allowed
+                    logger.warning("ManagedServer not available. Falling back to direct server mode.")
+                    agent = HermesAgentLoop(
+                        server=self.server,
+                        tool_schemas=tools,
+                        valid_tool_names=valid_names,
+                        max_turns=self.config.max_agent_turns,
+                        task_id=task_id,
+                        temperature=self.config.agent_temperature,
+                        max_tokens=self.config.max_token_length,
+                        extra_body=self.config.extra_body,
+                    )
+                    result = await agent.run(messages)
+            else:
+                # Phase 1: OpenAI server
+                agent = HermesAgentLoop(
+                    server=self.server,
+                    tool_schemas=tools,
+                    valid_tool_names=valid_names,
+                    max_turns=self.config.max_agent_turns,
+                    task_id=task_id,
+                    temperature=self.config.agent_temperature,
+                    max_tokens=self.config.max_token_length,
+                    extra_body=self.config.extra_body,
+                )
+                result = await agent.run(messages)
+
+            # Skip reward computation if agent produced no output
+            only_system_and_user = all(
+                msg.get("role") in ("system", "user") for msg in result.messages
+            )
+            if result.turns_used == 0 or only_system_and_user:
+                logger.warning(
+                    "Agent loop produced no output (turns=%d). Skipping trajectory.",
+                    result.turns_used,
+                )
+                # Return None to skip this trajectory (likely an API failure)
+                return None, []
+            else:
+                # Compute reward using ToolContext
+                ctx = ToolContext(task_id)
+                try:
+                    reward = await self.compute_reward(item, result, ctx)
+                except Exception as e:
+                    logger.error("compute_reward failed: %s", e)
+                    reward = 0.0
+                finally:
+                    ctx.cleanup()
+
+            # Track metrics for wandb logging
+            task_metrics = {
+                "test_passed": 1.0 if reward > 0.5 else 0.0,
+                "reward": reward,
+                "turns_used": result.turns_used,
+                "finished_naturally": result.finished_naturally,
+                "docker_image": docker_image,
+                "num_tool_errors": len(result.tool_errors),
+            }
+
+            # Include detailed tool errors if any occurred
+            if result.tool_errors:
+                task_metrics["tool_errors"] = [
+                    {
+                        "turn": err.turn,
+                        "tool": err.tool_name,
+                        "error": err.error[:200],
+                    }
+                    for err in result.tool_errors
+                ]
+
+            self._metrics_buffer.append(task_metrics)
+
+            # ============================================================================
+            # Build ScoredDataGroup from ManagedServer state
+            # ============================================================================
+            # Phase 2: Extract pre-computed data from SequenceNodes
+            # We may have multiple trajectories in the nodes due to how interesting
+            # agents can be, so iterate through all nodes and return multiple sequences.
+            #
+            # Each SequenceNode contains:
+            # - tokens: Full unmasked token sequence [1, 2, 3, ..., N]
+            # - masked_tokens: Training format [-100, -100, ..., -100, actual, actual, ...]
+            # - logprobs: Training format [1.0, 1.0, ..., 1.0, -0.5, -0.3, ...]
+            # - full_text: Complete text (prompt + all completions)
+            #
+            # Phase 1: Create placeholder tokens for OpenAI-style servers
+            # ============================================================================
+            nodes = (managed_state or {}).get("nodes", []) if managed_state else []
+
+            # Create ScoredDataGroup with lists for multiple trajectories
+            scored_group = ScoredDataGroup()
+            scored_group["tokens"] = []
+            scored_group["masks"] = []
+            scored_group["scores"] = []
+            scored_group["messages"] = []
+            scored_group["inference_logprobs"] = []
+
+            if nodes:
+                # Phase 2: iterate through all nodes (may have multiple trajectories)
+                for i, node in enumerate(nodes):
+                    scored_group["tokens"].append(node.tokens)
+                    scored_group["masks"].append(node.masked_tokens)
+                    scored_group["scores"].append(reward)
+                    scored_group["messages"].append(result.messages)
+
+                    if hasattr(node, "logprobs") and node.logprobs:
+                        scored_group["inference_logprobs"].append(node.logprobs)
+                    else:
+                        # Placeholder logprobs if not available
+                        scored_group["inference_logprobs"].append([1.0] * len(node.tokens))
+
+                    logger.debug(f"Added trajectory {i+1}/{len(nodes)} with {len(node.tokens)} tokens")
+
+            else:
+                # Phase 1: create placeholder tokens for OpenAI-style servers
+                full_text = "\n".join(
+                    msg.get("content", "") for msg in result.messages if msg.get("content")
+                )
+                if self.tokenizer:
+                    tokens = self.tokenizer.encode(full_text, add_special_tokens=True)
+                else:
+                    tokens = list(range(min(len(full_text) // 4, 128)))
+
+                scored_group["tokens"].append(tokens)
+                scored_group["masks"].append([-100] + tokens[1:])
+                scored_group["scores"].append(reward)
+                scored_group["messages"].append(result.messages)
+                scored_group["inference_logprobs"].append([1.0] * len(tokens))
+
+            # Return None if no trajectories collected
+            if len(scored_group["tokens"]) == 0:
+                return None, []
+
+            logger.debug(f"Returning ScoredDataGroup with {len(scored_group['tokens'])} trajectories")
+            return scored_group, []
+
+        finally:
+            # Clean up task overrides and sandbox
+            clear_task_env_overrides(task_id)
+            try:
+                cleanup_vm(task_id)
+            except Exception as e:
+                logger.debug(f"VM cleanup for {task_id[:8]}: {e}")
+
+    async def compute_reward(
+        self,
+        item: Item,
+        result: AgentResult,
+        ctx: ToolContext
+    ) -> float:
+        """
+        Run final tests in the agent's sandbox and return binary reward.
+
+        Uses ToolContext to execute pytest in the SAME sandbox the agent used,
+        following the Terminal Bench 2 verification pattern. No separate
+        Apptainer execution needed.
+
+        Returns 1.0 if tests pass, 0.0 otherwise.
+        """
+        task_name = item.get("task_name", "unknown")
+        final_test_path = Path(item.get("final_test", ""))
+
+        if not final_test_path.exists():
+            logger.error(f"Task {task_name}: test file not found at {final_test_path}")
+            return 0.0
+
+        logger.info(f"Task {task_name}: running tests in sandbox...")
+
+        try:
+            # Run tests in a thread to avoid blocking the event loop
+            loop = asyncio.get_event_loop()
+            reward = await loop.run_in_executor(
+                None,
+                self._run_tests_in_sandbox,
+                final_test_path,
+                ctx,
+                task_name,
+            )
+
+            status = "PASS" if reward == 1.0 else "FAIL"
+            logger.info(f"Task {task_name}: {status} (reward={reward})")
+            return reward
+
+        except Exception as e:
+            logger.error(f"Task {task_name}: test execution failed: {e}", exc_info=True)
+            return 0.0
+
+    def _run_tests_in_sandbox(
+        self,
+        test_file_path: Path,
+        ctx: ToolContext,
+        task_name: str,
+    ) -> float:
+        """
+        Upload test file to sandbox and execute pytest.
+
+        Runs in thread pool (via run_in_executor) to avoid blocking the event loop
+        with synchronous ToolContext calls.
+
+        Args:
+            test_file_path: Local path to test_final_state.py
+            ctx: ToolContext scoped to the agent's sandbox
+            task_name: For logging
+
+        Returns:
+            1.0 if tests pass, 0.0 otherwise
+        """
+        try:
+            # Upload test file to sandbox
+            test_content = test_file_path.read_text()
+            ctx.write_file("/workspace/test_final_state.py", test_content)
+            logger.debug(f"Task {task_name}: uploaded test file to /workspace/test_final_state.py")
+
+            # Run pytest in the sandbox
+            result = ctx.terminal(
+                "cd /workspace && python -m pytest -q test_final_state.py",
+                timeout=self.config.test_timeout_s,
+            )
+
+            exit_code = result.get("exit_code", -1)
+            output = result.get("output", "")
+
+            if exit_code == 0:
+                logger.debug(f"Task {task_name}: tests passed")
+                return 1.0
+            else:
+                # Log failure output (last 500 chars for debugging)
+                output_preview = output[-500:] if output else "(no output)"
+                logger.info(
+                    f"Task {task_name}: tests failed (exit_code={exit_code})\n{output_preview}"
+                )
+                return 0.0
+
+        except Exception as e:
+            logger.error(f"Task {task_name}: error running tests: {e}")
+            return 0.0
+
+    async def evaluate(self):
+        """
+        Periodic evaluation on holdout eval set.
+
+        Runs the agent on num_eval_tasks from the held-out eval set
+        (never seen during training). Returns metrics for wandb logging.
+        """
+        if self._eval_dataset is None:
+            logger.warning("Cannot evaluate: eval dataset not loaded")
+            return {}
+
+        if len(self._eval_dataset) == 0:
+            logger.warning("Eval dataset is empty")
+            return {}
+
+        # Use min of num_eval_tasks and actual eval set size
+        num_tasks = min(self.config.num_eval_tasks, len(self._eval_dataset))
+        logger.info(f"Starting evaluation on {num_tasks} held-out tasks...")
+
+        eval_metrics = {
+            "rewards": [],
+            "passes": [],
+            "turns": [],
+            "natural_finishes": [],
+        }
+
+        # Sample from eval set (holdout)
+        import random
+        eval_indices = random.sample(range(len(self._eval_dataset)), num_tasks)
+
+        for idx in eval_indices:
+            task = self._eval_dataset[idx]
+
+            # Build item using same logic as get_next_item
+            task_dir = task.get("extra_info", {}).get("task_dir")
+            if not task_dir:
+                task_dir = task.get("reward_spec", {}).get("ground_truth")
+
+            if not task_dir:
+                continue
+
+            task_dir_path = Path(task_dir)
+            if self.config.tasks_base_dir and not task_dir_path.exists():
+                original_path = Path(task_dir)
+                task_name = original_path.name
+                task_dir_path = Path(os.path.expanduser(self.config.tasks_base_dir)) / task_name
+
+            if not task_dir_path.exists():
+                continue
+
+            # Find test file
+            final_test = task_dir_path / "tests" / "test_final_state.py"
+            if not final_test.exists():
+                final_test = task_dir_path / "test_final_state.py"
+            if not final_test.exists():
+                continue
+
+            # Parse Docker image
+            container_def = task_dir_path / "environment" / "container.def"
+            if not container_def.exists():
+                container_def = task_dir_path / "container.def"
+            docker_image = self._parse_docker_image_from_def(container_def)
+
+            # Load description
+            description = task.get("description", "")
+            instruction_md = task_dir_path / "instruction.md"
+            if not description and instruction_md.exists():
+                try:
+                    description = instruction_md.read_text().strip()
+                except Exception:
+                    pass
+
+            item = {
+                "description": description,
+                "final_test": str(final_test),
+                "docker_image": docker_image,
+            }
+
+            # Run agent on this task
+            try:
+                import uuid
+                task_id = str(uuid.uuid4())
+
+                # Register task environment
+                from model_tools import register_task_env_overrides
+                register_task_env_overrides(task_id, {"modal_image": docker_image})
+
+                # Build messages
+                messages = [
+                    {"role": "system", "content": self.config.system_prompt},
+                    {"role": "user", "content": description or "Complete the task."},
+                ]
+
+                # Get tools
+                from model_tools import get_tool_definitions
+                tools = get_tool_definitions(self.config.enabled_toolsets)
+                valid_names = {t["function"]["name"] for t in tools}
+
+                # Run agent
+                from environments.agent_loop import HermesAgentLoop
+                agent = HermesAgentLoop(
+                    server=self.server,
+                    tool_schemas=tools,
+                    valid_tool_names=valid_names,
+                    max_turns=self.config.max_agent_turns,
+                    task_id=task_id,
+                    temperature=self.config.agent_temperature,
+                    max_tokens=self.config.max_token_length,
+                    extra_body=self.config.extra_body,
+                )
+                result = await agent.run(messages)
+
+                # Compute reward
+                from environments.tool_context import ToolContext
+                ctx = ToolContext(task_id)
+                try:
+                    reward = await self.compute_reward(item, result, ctx)
+                except Exception as e:
+                    logger.warning(f"Eval reward computation failed: {e}")
+                    reward = 0.0
+                finally:
+                    ctx.cleanup()
+
+                # Track metrics
+                eval_metrics["rewards"].append(reward)
+                eval_metrics["passes"].append(1.0 if reward > 0.5 else 0.0)
+                eval_metrics["turns"].append(result.turns_used)
+                eval_metrics["natural_finishes"].append(1.0 if result.finished_naturally else 0.0)
+
+            except Exception as e:
+                logger.error(f"Eval task failed: {e}")
+                continue
+            finally:
+                # Cleanup
+                from model_tools import clear_task_env_overrides, cleanup_vm
+                clear_task_env_overrides(task_id)
+                cleanup_vm(task_id)
+
+        # Aggregate metrics
+        if not eval_metrics["rewards"]:
+            logger.warning("No eval tasks completed successfully")
+            return {}
+
+        aggregated = {
+            "eval/pass_rate": sum(eval_metrics["passes"]) / len(eval_metrics["passes"]),
+            "eval/avg_reward": sum(eval_metrics["rewards"]) / len(eval_metrics["rewards"]),
+            "eval/avg_turns": sum(eval_metrics["turns"]) / len(eval_metrics["turns"]),
+            "eval/natural_finish_rate": sum(eval_metrics["natural_finishes"]) / len(eval_metrics["natural_finishes"]),
+            "eval/num_tasks": len(eval_metrics["rewards"]),
+        }
+
+        logger.info(f"Evaluation complete: pass_rate={aggregated['eval/pass_rate']:.2%}, avg_turns={aggregated['eval/avg_turns']:.1f}")
+        return aggregated
+
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        """Log Endless Terminals specific metrics to wandb."""
+        if wandb_metrics is None:
+            wandb_metrics = {}
+
+        # Aggregate metrics from buffer
+        if self._metrics_buffer:
+            # Test pass rate
+            test_passes = [m["test_passed"] for m in self._metrics_buffer]
+            wandb_metrics["endless_terminals/test_pass_rate"] = sum(test_passes) / len(test_passes)
+            wandb_metrics["endless_terminals/num_tests_passed"] = sum(test_passes)
+            wandb_metrics["endless_terminals/num_tests_total"] = len(test_passes)
+
+            # Turns used statistics
+            turns = [m["turns_used"] for m in self._metrics_buffer]
+            wandb_metrics["endless_terminals/avg_turns_used"] = sum(turns) / len(turns)
+            wandb_metrics["endless_terminals/max_turns_used"] = max(turns)
+            wandb_metrics["endless_terminals/min_turns_used"] = min(turns)
+
+            # Natural finish rate (did model stop on its own vs hitting max turns)
+            natural_finishes = [1.0 if m["finished_naturally"] else 0.0 for m in self._metrics_buffer]
+            wandb_metrics["endless_terminals/natural_finish_rate"] = sum(natural_finishes) / len(natural_finishes)
+
+            # Tool error statistics
+            total_tool_errors = sum(m["num_tool_errors"] for m in self._metrics_buffer)
+            wandb_metrics["endless_terminals/total_tool_errors"] = total_tool_errors
+            wandb_metrics["endless_terminals/avg_tool_errors_per_task"] = total_tool_errors / len(self._metrics_buffer)
+
+            # Docker image distribution (count unique images used)
+            docker_images = [m["docker_image"] for m in self._metrics_buffer]
+            unique_images = set(docker_images)
+            wandb_metrics["endless_terminals/num_unique_docker_images"] = len(unique_images)
+
+            # Log most common errors if any
+            all_errors = []
+            for m in self._metrics_buffer:
+                if "tool_errors" in m:
+                    all_errors.extend(m["tool_errors"])
+
+            if all_errors:
+                # Count error types
+                error_tools = {}
+                for err in all_errors:
+                    tool = err["tool"]
+                    error_tools[tool] = error_tools.get(tool, 0) + 1
+
+                # Log top 3 error-prone tools
+                for i, (tool, count) in enumerate(sorted(error_tools.items(), key=lambda x: x[1], reverse=True)[:3]):
+                    wandb_metrics[f"endless_terminals/errors_by_tool/{tool}"] = count
+
+            # Clear buffer after logging
+            self._metrics_buffer = []
+
+        await super().wandb_log(wandb_metrics)
+
+
+if __name__ == "__main__":
+    EndlessTerminalsEnv.cli()