Revert "Weighted reward functions"

recipes/DeepSeek-R1-Distill-Qwen-7B/grpo/config_demo.yaml

@@ -40,12 +40,6 @@ per_device_train_batch_size: 16
 push_to_hub: true
 report_to:
 - wandb
-reward_funcs:
-  - accuracy
-  - format
-reward_weights:
-  - 1.0
-  - 1.0
 save_strategy: "no"
 seed: 42
 warmup_ratio: 0.1

recipes/Qwen2.5-1.5B-Instruct/grpo/config_demo.yaml

@@ -42,12 +42,6 @@ per_device_train_batch_size: 16
 push_to_hub: true
 report_to:
 - wandb
-reward_funcs:
-  - accuracy
-  - format
-reward_weights:
-  - 1.0
-  - 1.0
 save_strategy: "no"
 seed: 42
 warmup_ratio: 0.1

recipes/Qwen2.5-Math-7B/grpo/config_simple_rl.yaml

@@ -42,12 +42,6 @@ per_device_train_batch_size: 16
 push_to_hub: true
 report_to:
 - wandb
-reward_funcs:
-  - accuracy
-  - format
-reward_weights:
-  - 1.0
-  - 1.0
 save_strategy: "no"
 seed: 42
 warmup_ratio: 0.1

src/open_r1/grpo.py

@@ -16,7 +16,6 @@
 import os
 import sys
 from dataclasses import dataclass, field
-from typing import Optional
 
 import datasets
 import torch
@@ -28,7 +27,6 @@
 from open_r1.configs import GRPOConfig
 from open_r1.rewards import (
     accuracy_reward,
-    create_weighted_reward,
     format_reward,
     get_cosine_scaled_reward,
     get_repetition_penalty_reward,
@@ -51,8 +49,6 @@ class GRPOScriptArguments(ScriptArguments):
     Args:
         reward_funcs (`list[str]`):
             List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'.
-        reward_weights (`list[float]` or `None`, *optional*):
-            List of weights for each reward function. If not provided, defaults to 1.0 for each reward function.
         cosine_min_value_wrong (`float`):
             Minimum reward for cosine scaling for wrong answers.
         cosine_max_value_wrong (`float`):
@@ -71,12 +67,6 @@ class GRPOScriptArguments(ScriptArguments):
             "help": "List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'"
         },
     )
-    reward_weights: Optional[list[float]] = field(
-        default=None,
-        metadata={
-            "help": "List of weights for each reward function. If not provided, defaults to 1.0 for each function."
-        },
-    )
     cosine_min_value_wrong: float = field(
         default=0.0,
         metadata={"help": "Minimum reward for wrong answers"},
@@ -98,17 +88,6 @@ class GRPOScriptArguments(ScriptArguments):
         metadata={"help": "Maximum length for scaling"},
     )
 
-    def __post_init__(self):
-        # If no weights were provided, default to 1.0 for each reward function
-        if self.reward_weights is None:
-            self.reward_weights = [1.0] * len(self.reward_funcs)
-        # If weights were provided, validate the length
-        elif len(self.reward_weights) != len(self.reward_funcs):
-            raise ValueError(
-                f"Number of reward weights ({len(self.reward_weights)}: {self.reward_weights}) must match "
-                f"number of reward functions ({len(self.reward_funcs)}: {self.reward_funcs})"
-            )
-
     repetition_n_grams: int = field(
         default=3,
         metadata={"help": "Number of n-grams for repetition penalty reward"},
@@ -168,7 +147,7 @@ def main(script_args, training_args, model_args):
     # Load the dataset
     dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
 
-    # Create weighted reward functions
+    # Get reward functions
     REWARD_FUNCS_REGISTRY = {
         "accuracy": accuracy_reward,
         "format": format_reward,
@@ -186,10 +165,7 @@ def main(script_args, training_args, model_args):
         ),
         "length": len_reward,
     }
-    reward_funcs = [
-        create_weighted_reward(REWARD_FUNCS_REGISTRY[func], weight)
-        for func, weight in zip(script_args.reward_funcs, script_args.reward_weights)
-    ]
+    reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]
 
     # Format into conversation
     def make_conversation(example):

src/open_r1/rewards.py

@@ -2,7 +2,6 @@
 
 import math
 import re
-from functools import wraps
 from typing import Dict
 
 from latex2sympy2_extended import NormalizationConfig
@@ -272,22 +271,3 @@ def repetition_penalty_reward(completions, **kwargs) -> float:
         return rewards
 
     return repetition_penalty_reward
-
-
-def create_weighted_reward(func, weight):
-    """Create a weighted version of a reward function.
-
-    Args:
-        func: The reward function to weight
-        weight: The weight to apply to the reward
-
-    Returns:
-        A new function that applies the weight to the reward
-    """
-
-    @wraps(func)
-    def weighted_reward(*args, **kwargs):
-        rewards = func(*args, **kwargs)
-        return [r * weight for r in rewards]
-
-    return weighted_reward

tests/test_rewards.py

@@ -2,7 +2,6 @@
 
 from open_r1.rewards import (
     accuracy_reward,
-    create_weighted_reward,
     format_reward,
     get_cosine_scaled_reward,
     get_repetition_penalty_reward,
@@ -77,35 +76,6 @@ def test_multiple_completions(self):
         self.assertEqual(rewards[0], 1.0)
         self.assertEqual(rewards[1], 0.0)
 
-    def test_weighted_reward(self):
-        """Test create_weighted_reward with different weights."""
-        # Test with weight = 2.0
-        completion = [[{"content": "<think>Some reasoning</think><answer>The answer</answer>"}]]
-        base_reward_func = format_reward
-        weighted_reward_func = create_weighted_reward(base_reward_func, 2.0)
-
-        base_rewards = base_reward_func(completion)
-        weighted_rewards = weighted_reward_func(completion)
-
-        self.assertEqual(base_rewards[0], 1.0)
-        self.assertEqual(weighted_rewards[0], 2.0)
-
-        # Test with weight = 0.5
-        weighted_reward_func = create_weighted_reward(base_reward_func, 0.5)
-        weighted_rewards = weighted_reward_func(completion)
-        self.assertEqual(weighted_rewards[0], 0.5)
-
-        # Test with multiple completions
-        completions = [
-            [{"content": "<think>Some reasoning</think><answer>The answer</answer>"}],
-            [{"content": "Invalid format"}],
-        ]
-        weighted_reward_func = create_weighted_reward(base_reward_func, 2.0)
-        weighted_rewards = weighted_reward_func(completions)
-
-        self.assertEqual(weighted_rewards[0], 2.0)
-        self.assertEqual(weighted_rewards[1], 0.0)
-
     def test_cosine_scaled_reward(self):
         """Test cosine_scaled_reward with various cases."""
         # Test parameters
@@ -141,17 +111,6 @@ def test_format_reward_specific_multiline(self):
         rewards = format_reward(completion)
         self.assertEqual(rewards[0], 1.0)
 
-    def test_weighted_reward_preserves_name(self):
-        """Test that create_weighted_reward preserves the original function name. Important for logging."""
-        base_reward_func = format_reward
-        weighted_reward_func = create_weighted_reward(base_reward_func, 2.0)
-
-        self.assertEqual(
-            weighted_reward_func.__name__,
-            base_reward_func.__name__,
-            "Weighted reward function should preserve the original function name",
-        )
-
     def test_same_length_responses(self):
         """Test len_reward when all responses have the same length."""
         completions = [[{"content": r"\boxed{\frac{63}{400}}"}], [{"content": r"\boxed{\frac{64}{400}}"}]]