diff --git a/environments/eval_environments/verifiers_eval.py b/environments/eval_environments/verifiers_eval.py
index 6d7c3755..4efcd26e 100644
--- a/environments/eval_environments/verifiers_eval.py
+++ b/environments/eval_environments/verifiers_eval.py
@@ -26,6 +26,7 @@ import verifiers as vf
 from pydantic import Field
 from tqdm.asyncio import tqdm_asyncio
 
+import wandb
 from atroposlib.envs.base import (
     APIServerConfig,
     BaseEnv,
@@ -46,13 +47,10 @@ class VerifiersEvaluationConfig(BaseEnvConfig):
         description="Additional arguments for verifiers environment",
     )
 
-    # Generation parameters
     temperature: float = Field(
         default=0.0, description="Temperature for generation (0.0 for deterministic)"
     )
-    max_tokens: int = Field(default=2048, description="Maximum tokens for generation")
 
-    # Retry and debug configuration
     max_retries: int = Field(
         default=3, description="Maximum retries for failed API calls"
     )
@@ -64,16 +62,6 @@ class VerifiersEvaluationConfig(BaseEnvConfig):
     )
     full_debug: bool = Field(default=False, description="Enable full debug output")
 
-    # Override defaults for evaluation mode
-    group_size: int = 1
-    max_num_workers: int = 256
-    max_num_workers_per_node: int = 64
-    use_wandb: bool = True
-    rollout_server_url: str = "http://localhost:8000"
-    total_steps: int = 1
-    wandb_name: str = "verifiers_evaluation"
-    steps_per_eval: int = 1
-
 
 class VerifiersEvaluationEnv(BaseEnv):
     """
@@ -118,17 +106,11 @@ class VerifiersEvaluationEnv(BaseEnv):
         """Default configuration for evaluation."""
         env_config = VerifiersEvaluationConfig(
             vf_env_name="primeintellect/gsm8k",
-            temperature=0.0,
-            max_tokens=2048,
-            use_wandb=True,
-            wandb_name="verifiers_evaluation",
         )
         server_configs = [
             APIServerConfig(
                 model_name="gpt-4.1-nano",
-                base_url=None,
                 api_key=os.getenv("OPENAI_API_KEY"),
-                num_requests_for_eval=256,
             ),
         ]
         return env_config, server_configs
@@ -169,7 +151,7 @@ class VerifiersEvaluationEnv(BaseEnv):
         kwargs = {
             "messages": messages,
             "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
+            "max_tokens": self.config.max_token_length,
             "n": 1,
         }
 
@@ -323,23 +305,42 @@ class VerifiersEvaluationEnv(BaseEnv):
             end_time=end_time,
             generation_parameters={
                 "temperature": self.config.temperature,
-                "max_tokens": self.config.max_tokens,
+                "max_tokens": self.config.max_token_length,
             },
         )
 
+        # Log to wandb
+        await self.wandb_log(metrics)
+
         return metrics
 
     async def wandb_log(self, wandb_metrics: Optional[Dict] = None) -> None:
         """Log metrics to Weights & Biases."""
-        if wandb_metrics is None:
-            wandb_metrics = {}
+        if not self.config.use_wandb or wandb_metrics is None:
+            return
 
-        # Add config info
-        wandb_metrics["config/vf_env_name"] = self.config.vf_env_name
-        wandb_metrics["config/temperature"] = self.config.temperature
-        wandb_metrics["config/max_tokens"] = self.config.max_tokens
+        # Lazy init if wandb not already initialized
+        if wandb.run is None:
+            wandb.init(
+                project="verifiers-eval",
+                name=self.config.wandb_name,
+                config=self.config.model_dump(),
+            )
 
-        await super().wandb_log(wandb_metrics)
+        log_dict = {
+            "verifiers/accuracy": wandb_metrics.get("accuracy", 0),
+            "verifiers/avg_score": wandb_metrics.get("avg_score", 0),
+            "verifiers/total_evaluated": wandb_metrics.get("total_evaluated", 0),
+            "verifiers/total_correct": wandb_metrics.get("total_correct", 0),
+        }
+
+        # Add per-reward function metrics
+        reward_breakdown = wandb_metrics.get("reward_breakdown", {})
+        for func_name, data in reward_breakdown.items():
+            log_dict[f"verifiers/{func_name}_avg"] = data.get("avg", 0)
+            log_dict[f"verifiers/{func_name}_correct"] = data.get("correct", 0)
+
+        wandb.log(log_dict)
 
     # Required abstract method implementations (stubs for evaluation-only mode)
     async def get_next_item(self) -> Optional[Dict]: