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Implementing custom loss functions for alignment tuning algorithms

Written by Nikhil Badjatya
Direct Summary:

Direct Preference Optimization (DPO) is the standard real-world example of a "custom loss function for alignment": instead of training a separate reward model and running PPO reinforcement learning against it, DPO's paper (Rafailov et al., 2023) shows you can reparameterize the RLHF objective into a single closed-form loss — a log-sigmoid of the difference between how much the model's preference for the "chosen" response shifted versus the "rejected" one, relative to a frozen reference model. Hugging Face's TRL library implements this directly as `DPOTrainer`, so you can align a model on a preference dataset without ever building a reward model.

"Whether you think you can or you think you can't, you're right."

— Henry Ford

Key Insights

  • DPO skips the reward model entirely: PPO-based RLHF needs a separately trained reward model plus RL training; DPO's paper shows the same optimum can be reached with one supervised-style loss on preference pairs directly.
  • The loss compares policy vs. reference model, not just chosen vs. rejected text: it's a log-sigmoid of the difference in how much each response's likelihood shifted relative to a frozen copy of the starting model — this is what keeps training stable.
  • You don't have to write it from scratch: TRL's `DPOTrainer` already implements this loss — "implementing a custom loss function" in practice usually means configuring and calling it correctly, not deriving the math yourself.

"Custom loss function for alignment" sounds like it requires deriving new math, but the most impactful real example — DPO — is already implemented and battle-tested in open-source tooling. Understanding what its loss actually does, though, is what lets you use it correctly instead of treating it as a black box.

How DPO's loss function works, and how to use it

1. Start from a preference dataset. You need pairs of (prompt, chosen response, rejected response) — human or model-generated preference judgments, not just plain text.

2. The loss scores relative preference shift against a frozen reference model. For each pair, DPO's loss increases the policy model's relative likelihood of the chosen response and decreases it for the rejected one, measured against how the unchanged reference model would have scored them — this reference term is what prevents the model from drifting arbitrarily far from its starting behavior.

3. Run it via TRL's `DPOTrainer` rather than reimplementing the math. The trainer handles the reference-model forward pass and loss computation for you; your job is preparing the preference dataset and setting the beta (KL-penalty strength) hyperparameter.

dpo_alignment.py 
# DPO training via TRL -- no separate reward model needed
from trl import DPOTrainer, DPOConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("base-sft-model")
tokenizer = AutoTokenizer.from_pretrained("base-sft-model")

# preference_dataset rows: {"prompt", "chosen", "rejected"}
config = DPOConfig(beta=0.1, output_dir="./dpo-out")
trainer = DPOTrainer(
    model=model, args=config, tokenizer=tokenizer,
    train_dataset=preference_dataset,
)
trainer.train()
Alignment Method Requires a Separate Reward Model?
PPO-based RLHF Yes — trained first, then used to score rollouts during RL
DPO No — the closed-form loss trains directly on preference pairs

The "custom loss function" framing is useful precisely because it clarifies what DPO actually replaced: not the goal of RLHF (aligning outputs to preferences), but the expensive two-stage reward-model-plus-RL machinery used to get there. Knowing the loss's mechanics — chosen vs. rejected, scored relative to a frozen reference — is what lets you debug a DPO run that isn't converging, rather than just treating `DPOTrainer` as a button to press.

Practical Challenge

Build a small preference dataset (10-20 prompt/chosen/rejected triples) for a task you care about, and run TRL's `DPOTrainer` against a small local model to see the loss decrease.

Concept Check

What does DPO's loss function eliminate the need for, compared to standard PPO-based RLHF?
Correct! DPO reparameterizes the RLHF objective into a direct, closed-form loss on preference pairs, removing the separate reward-model-training and RL stages PPO requires.
Incorrect. Try again! DPO still needs preference data and a starting base model — what it removes is the separate reward model and RL loop.

Sources & Further Reading

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