"""
RLHF (Reinforcement Learning from Human Feedback)
-------------------------------------------------
A technique where models are trained using feedback from humans to align outputs with human preferences.

Example usage:
    >>> import torch
    >>> from langvision.concepts.rlhf import RLHF
    >>> class MyRLHF(RLHF):
    ...     def train(self, model, data, feedback_fn, optimizer):
    ...         super().train(model, data, feedback_fn, optimizer)
    >>> model = torch.nn.Linear(2, 1)
    >>> data = [torch.tensor([1.0, 2.0]), torch.tensor([2.0, 3.0])]
    >>> def feedback_fn(output): return 1.0 if output.item() > 0 else -1.0
    >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    >>> rlhf = MyRLHF()
    >>> rlhf.train(model, data, feedback_fn, optimizer)
"""
from abc import ABC, abstractmethod
from typing import Any, Callable, List
import torch

class RLHF(ABC):
    """
    Abstract base class for Reinforcement Learning from Human Feedback (RLHF).
    """
    @abstractmethod
    def train(self, model: torch.nn.Module, data: List[Any], feedback_fn: Callable[[Any], float], optimizer: torch.optim.Optimizer) -> None:
        """
        Train the model using data and a feedback function that simulates human feedback.
        """
        for x in data:
            optimizer.zero_grad()
            output = model(x)
            # Synthetic feedback as reward
            reward = feedback_fn(output)
            # Simple loss: negative reward (maximize reward)
            loss = -reward * output.sum()
            loss.backward()
            optimizer.step() 