ff-new-reward

name	ff-new-reward
description	Complete workflow for adding a new reward model. Covers pointwise vs groupwise design, __call__ contract, registration, YAML config, multi-reward setup, and verification. Trigger: 'add reward', 'new reward model', 'custom reward', 'scoring function'.

New Reward Model Integration

Authoritative reference: guidance/rewards.md — read it first. Template: src/flow_factory/rewards/my_reward.py

Prerequisites

Determine your reward type:

• Pointwise: Each sample scored independently (e.g., aesthetic score, CLIP similarity)
• Groupwise: Scores depend on comparison within a group (e.g., ranking, preference)

Phase 1: Design

1. Choose base class: PointwiseRewardModel or GroupwiseRewardModel
2. Identify required inputs: What fields from Sample does your reward need?
   • Common: prompt, image, video, condition_images, condition_videos
   • Set required_fields tuple accordingly
3. Input format: PIL Images (default) or Tensors?
   • Set use_tensor_inputs = True if your model needs raw tensors

Phase 2: Implementation

Create the reward model file

# src/flow_factory/rewards/<my_reward>.py
from .abc import PointwiseRewardModel, RewardModelOutput
from ..hparams import RewardArguments
from accelerate import Accelerator
from typing import Optional, List
from PIL import Image
import torch

class MyRewardModel(PointwiseRewardModel):
    required_fields = ("prompt", "image")
    use_tensor_inputs = False

    def __init__(self, config: RewardArguments, accelerator: Accelerator):
        super().__init__(config, accelerator)
        # Load your model, processor, etc.
        # Use self.device and self.dtype from base class

    @torch.no_grad()
    def __call__(
        self,
        prompt: List[str],
        image: Optional[List[Image.Image]] = None,
        video: Optional[List[List[Image.Image]]] = None,
        audio: Optional[List[torch.Tensor]] = None,
        condition_images=None,
        condition_videos=None,
        **kwargs,
    ) -> RewardModelOutput:
        # Compute rewards — shape must be (batch_size,) for Pointwise
        # or (group_size,) for Groupwise
        rewards = torch.zeros(len(prompt), device=self.device)
        return RewardModelOutput(rewards=rewards)

Key constraints for __call__:

• Pointwise: Input length = config.batch_size. Return rewards shape (batch_size,)
• Groupwise: Input length = group_size. You handle batching yourself. Return rewards shape (group_size,)
• Always use @torch.no_grad() decorator
• Return RewardModelOutput (not raw tensors)

Phase 3: Register

Add to _REWARD_MODEL_REGISTRY in src/flow_factory/rewards/registry.py:

'my_reward': 'flow_factory.rewards.<my_reward>.MyRewardModel',

Phase 4: Configuration

Use in YAML config:

rewards:
  - name: "my_reward"
    reward_model: "my_reward"        # Must match registry key
    model_path: "org/model-name"     # HuggingFace model path (if applicable)
    dtype: "bfloat16"
    device: "cuda"
    batch_size: 16

Multi-reward setup:

rewards:
  - name: "aesthetic"
    reward_model: "PickScore"
    weight: 0.7
  - name: "custom"
    reward_model: "my_reward"
    weight: 0.3

Phase 5: Verification

• __init__ loads model without errors
• __call__ returns correct reward shape
• Rewards are numerically reasonable (not all zeros, no NaN/Inf)
• Works with RewardProcessor dispatch (Pointwise/Groupwise routing)
• Works in multi-reward setup with weight aggregation
• Device placement correct (respects config.device)
• Registry entry resolves: get_reward_model_class('my_reward')

Common Pitfalls

1. Wrong return shape — Pointwise must return (batch_size,), Groupwise (group_size,)
2. Forgetting @torch.no_grad() — causes reward computation to build unnecessary graph, OOM
3. Hardcoding device — use self.device from base class, not torch.device('cuda')
4. Not setting required_fields — RewardProcessor won't pass the right data to your model
5. Mixing paradigms — don't inherit PointwiseRewardModel if your reward needs group context