For proposer, solver model, skip file exists check as they might be on HF hub.

Author: abhishekraok

wandering_light/evals/evaluate_proposer.py

@@ -290,15 +290,9 @@ def file_evaluate_proposer(
     if isinstance(model, str):
         model = TrainedLLMTokenGenerator(model)
     if isinstance(solver_model, str):
-        if os.path.exists(solver_model):
-            solver_model = create_token_solver(
-                TrainedLLMTokenGenerator(solver_model), budget=1
-            )
-        else:
-            print(
-                f"Solver model not found at {solver_model}, skipping solver based evaluation"
-            )
-            solver_model = None
+        solver_model = create_token_solver(
+            TrainedLLMTokenGenerator(solver_model), budget=1
+        )
 
     trajectories = TrajectoryList.from_file(eval_file)
     num_samples = num_samples or len(trajectories)

wandering_light/training/rl_grpo.py

@@ -200,18 +200,11 @@ def _load_eval_data(self):
 
         # Load solver model for proposer task
         if self.task == Task.PROPOSER:
-            if os.path.exists(DEFAULT_SOLVER_CHECKPOINT):
-                self.solver_model = create_token_solver(
-                    TrainedLLMTokenGenerator(
-                        DEFAULT_SOLVER_CHECKPOINT, temperature=0.8
-                    ),
-                    budget=1,
-                )
-                logger.info(f"Loaded solver model from {DEFAULT_SOLVER_CHECKPOINT}")
-            else:
-                logger.warning(
-                    "Solver checkpoint not found, skipping solver based evaluation for proposer task"
-                )
+            self.solver_model = create_token_solver(
+                TrainedLLMTokenGenerator(DEFAULT_SOLVER_CHECKPOINT, temperature=0.8),
+                budget=1,
+            )
+            logger.info(f"Loaded solver model from {DEFAULT_SOLVER_CHECKPOINT}")
 
     def _run_evaluation(self, model_path: str):
         """Run evaluation on the current model using pre-computed trajectories."""

wandering_light/training/sft.py

@@ -54,14 +54,9 @@ def _load_eval_data(self):
             self.trajectories = None
             self.available_functions = None
         if self.task == Task.PROPOSER:
-            if os.path.exists(DEFAULT_SOLVER_CHECKPOINT):
-                self.solver_model = create_token_solver(
-                    TrainedLLMTokenGenerator(DEFAULT_SOLVER_CHECKPOINT), budget=1
-                )
-            else:
-                print(
-                    "Solver checkpoint not found, skipping solver based evaluation for proposer task"
-                )
+            self.solver_model = create_token_solver(
+                TrainedLLMTokenGenerator(DEFAULT_SOLVER_CHECKPOINT), budget=1
+            )
 
     def _run_evaluation(self, model_path: str):
         """Run evaluation on the current model using pre-computed trajectories."""