RIFT

Gold definitionUpdated Apr 2, 2026

Definition

RIFT (Reward Informed Fine-Tuning) is an LLM alignment framework that efficiently uses all self-generated samples, including negative ones, by reweighting the loss with scalar rewards. It addresses data inefficiency and improves robustness over methods like RFT by employing a stabilized loss formulation.

At a glance

Executive summary

RIFT (Reward Informed Fine-Tuning) is a new way to train AI language models more efficiently. It teaches models by using all their self-generated responses, good and bad, adjusting how much the model learns from each based on a reward score. This helps models learn better from less data and avoids issues seen in other training methods.

TL;DR

RIFT is a smart way to train AI language models that uses all the model's practice attempts, even the wrong ones, to make them better and more efficient.

Key points

Trains LLMs by utilizing all self-generated samples (positive and negative) and reweighting loss with scalar rewards, using a stabilized loss formulation.
Solves data inefficiency and reliance on costly expert data in LLM alignment, offering a robust alternative to SFT and RFT.
Used by researchers and ML engineers for LLM alignment, particularly for tasks requiring robust learning from mixed-quality, self-generated data like mathematical reasoning.
Unlike Rejection Sampling Fine-Tuning (RFT) which discards negative samples, RIFT repurposes them, leading to greater data efficiency and improved performance.
Represents a research trend towards data-efficient and robust LLM alignment techniques, especially for learning from self-generated or mixed-quality data without expensive human annotation.

Use cases

Improving mathematical reasoning capabilities in LLMs by learning from both correct and incorrect solution attempts.
Aligning LLMs for complex code generation tasks, where varied quality of generated code can be used to refine the model.
Developing more robust chatbots or conversational agents by leveraging diverse user interactions, including less optimal responses, to refine dialogue policy.
Fine-tuning LLMs for scientific discovery tasks where generating and evaluating hypotheses can provide mixed-quality data for learning.

Also known as

RIFT, Reward Informed Fine-Tuning