feat: integrate FinWorldJudge with OpenJudge support & add project blogs

ajet/utils/metric_helper/reward_metric_helper.py

@@ -2,12 +2,16 @@
 deep_finance Reward Metrics Helper
 
 Provides standalone utility functions for reward_stats extraction and SwanLab metrics formatting.
-Decouples deep_finance-specific logic from core code, reducing intrusion into native_compat_trainer.
+
+Data sources:
+1. Finance Evaluator (finance_raw, finance_contribution)
+2. OpenJudge Graders (openjudge_xxx_raw, openjudge_xxx_contribution)
 
 SwanLab metrics directory structure:
 - rewards/                    Top-level aggregated scores
-- rewards/dimensions/         Raw scores (unweighted)
-- rewards/contribution/       Weighted contributions
+- rewards/dimensions/         Raw scores (unweighted): finance_raw, openjudge_*_raw
+- rewards/contribution/       Weighted contributions: finance_contribution, openjudge_*_contribution  
+- rewards/openjudge/          OpenJudge grader specific metrics
 - judge_time/                 Judge time consumption statistics
 """
 
@@ -41,9 +45,9 @@ def compute_reward_metrics(reward_stats_list: List[Dict[str, Any]], prefix: str
     """
     Compute SwanLab metrics from reward_stats list.
 
-    Supports two data sources:
-    1. RM Gallery RewardStats fields (rm_raw, etc.)
-    2. OpenJudge fields (openjudge_xxx_raw, openjudge_xxx_contribution, etc.)
+    Data sources:
+    1. Finance Evaluator (finance_raw, finance_contribution)
+    2. OpenJudge Graders (openjudge_xxx_raw, openjudge_xxx_contribution)
 
     Args:
         reward_stats_list: List of reward_stats dictionaries
@@ -72,61 +76,46 @@ def compute_reward_metrics(reward_stats_list: List[Dict[str, Any]], prefix: str
     metrics[f"{prefix}rewards/fused_reward_mean"] = float(np.mean(fused_reward_list))
     metrics[f"{prefix}rewards/penalty_mean"] = float(np.mean(penalty_list))
     metrics[f"{prefix}rewards/step_reward_mean"] = float(np.mean(step_reward_list))
-    metrics[f"{prefix}rewards/penalty_count"] = len(non_zero_penalties)
-    metrics[f"{prefix}rewards/penalty_rate"] = len(non_zero_penalties) / n * 100 if n > 0 else 0.0
-
-    # ========== OpenJudge Metrics (PresentationQualityGrader, GroundingGrader) ==========
-    openjudge_enabled_count = sum(1 for rs in reward_stats_list if rs.get('openjudge_enabled', False))
-
-    if openjudge_enabled_count > 0:
-        # OpenJudge graders: presentation_quality, grounding
-        openjudge_graders = [
-            "presentation_quality",
-            "grounding",
-            "planning",
-            "audit",
-            "traceability",
-            "cgcv"
-        ]
-
-        for grader_name in openjudge_graders:
-            raw_key = f"openjudge_{grader_name}_raw"
-            contrib_key = f"openjudge_{grader_name}_contribution"
-
-            raw_list = [rs.get(raw_key, 0.0) for rs in reward_stats_list]
-            contrib_list = [rs.get(contrib_key, 0.0) for rs in reward_stats_list]
-
-            # Only report when non-zero values exist
-            if any(v != 0.0 for v in raw_list):
-                metrics[f"{prefix}rewards/openjudge/{grader_name}_raw_mean"] = float(np.mean(raw_list))
-            if any(v != 0.0 for v in contrib_list):
-                metrics[f"{prefix}rewards/openjudge/{grader_name}_contribution_mean"] = float(np.mean(contrib_list))
-
-        # OpenJudge time consumption statistics
-        grading_time_list = [rs.get('grading_time', 0.0) for rs in reward_stats_list]
-        if any(v != 0.0 for v in grading_time_list):
-            metrics[f"{prefix}judge_time/openjudge_grading_time_mean"] = float(np.mean(grading_time_list))
-            metrics[f"{prefix}judge_time/openjudge_grading_time_max"] = float(np.max(grading_time_list))
-
-    # ========== RM Gallery Metrics ==========
-
-    # RM Gallery
-    rm_raw_list = [rs.get('rm_raw', 0.0) for rs in reward_stats_list]
-    rm_contribution_list = [rs.get('rm_contribution', 0.0) for rs in reward_stats_list]
-
-    # dimensions/ raw scores
-    metrics[f"{prefix}rewards/dimensions/rm_raw_mean"] = float(np.mean(rm_raw_list))
-
-    # contribution/ weighted contributions
-    metrics[f"{prefix}rewards/contribution/rm_contribution_mean"] = float(np.mean(rm_contribution_list))
-
-
-    # Time consumption statistics
-    rm_time_list = [rs.get('rm_time', 0.0) for rs in reward_stats_list]
-    metrics[f"{prefix}judge_time/rm_time_mean"] = float(np.mean(rm_time_list))
-
-    if rm_time_list:
-        metrics[f"{prefix}judge_time/rm_time_max"] = float(np.max(rm_time_list))
+    metrics[f"{prefix}rewards/penalty_count"] = float(len(non_zero_penalties))
+    metrics[f"{prefix}rewards/penalty_rate"] = float(len(non_zero_penalties) / n * 100) if n > 0 else 0.0
+
+    # ========== OpenJudge Metrics ==========
+    # OpenJudge graders: presentation_quality, grounding, audit
+    openjudge_graders = [
+        "presentation_quality",
+        "grounding",
+        "planning",
+        "audit",
+    ]
+
+    for grader_name in openjudge_graders:
+        raw_key = f"openjudge_{grader_name}_raw"
+        contrib_key = f"openjudge_{grader_name}_contribution"
+
+        raw_list = [rs.get(raw_key, 0.0) for rs in reward_stats_list]
+        contrib_list = [rs.get(contrib_key, 0.0) for rs in reward_stats_list]
+
+        # Only report when non-zero values exist
+        if any(v != 0.0 for v in raw_list):
+            metrics[f"{prefix}rewards/openjudge/{grader_name}_raw_mean"] = float(np.mean(raw_list))
+        if any(v != 0.0 for v in contrib_list):
+            metrics[f"{prefix}rewards/openjudge/{grader_name}_contribution_mean"] = float(np.mean(contrib_list))
+
+    # OpenJudge time consumption statistics
+    grading_time_list = [rs.get('grading_time', 0.0) for rs in reward_stats_list]
+    if any(v != 0.0 for v in grading_time_list):
+        metrics[f"{prefix}judge_time/openjudge_grading_time_mean"] = float(np.mean(grading_time_list))
+        metrics[f"{prefix}judge_time/openjudge_grading_time_max"] = float(np.max(grading_time_list))
+
+    # ========== Finance Evaluator Metrics ==========
+    finance_raw_list = [rs.get('finance_raw', 0.0) for rs in reward_stats_list]
+    finance_contribution_list = [rs.get('finance_contribution', 0.0) for rs in reward_stats_list]
+
+    if any(v != 0.0 for v in finance_raw_list):
+        metrics[f"{prefix}rewards/dimensions/finance_raw_mean"] = float(np.mean(finance_raw_list))
+
+    if any(v != 0.0 for v in finance_contribution_list):
+        metrics[f"{prefix}rewards/contribution/finance_contribution_mean"] = float(np.mean(finance_contribution_list))
 
     # ========== General Time Consumption Statistics ==========
     judge_total_time_list = [rs.get('judge_total_time', 0.0) for rs in reward_stats_list]

tutorial/example_deep_finance/.env.example

@@ -0,0 +1,28 @@
+# API keys
+OPENAI_API_KEY="sk-xxx"
+OPENAI_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
+RM_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
+RM_API_KEY="sk-xxx" 
+OPENJUDGE_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
+OPENJUDGE_API_KEY="sk-xxx" 
+STRONG_MODEL_API_KEY="sk-xxx"
+
+SWANLAB_API_KEY="xxx"
+
+# data path, save path
+ENV_SERVICE_ROOT="/path/to/env_service"
+CONDA_PATH="/path/to/conda/conda.sh"
+MODEL_PATH="/path/to/base_model"
+CKPT_SAVE_PATH="/path/to/ckpt_path"
+# 新增：数据文件路径配置
+TRAIN_DATA_PATH="/path/to/train_data"
+VAL_DATA_PATH="/path/to/val_data"
+
+
+TRAIN_REF_ANS_PATH="/path/to/train_reference_answer"
+VAL_REF_ANS_PATH="/path/to/val_reference_answer"
+
+
+# Port
+ADDR=""
+MCP_PORT=""