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π Published on October 8, 2024
One of the most notable inventions this year is Reinforcement Learning Machine Feedback (RLMF), which has attracted significant attention. Interestingly, it may not even be the most groundbreaking development, but its impact is undeniable.
RLMF is essentially an enhanced version of a Generative Adversarial Network (GAN), designed to automatically improve large language models (LLMs). Many prestigious labs are now using similar techniques to benchmark their foundational models.
In a traditional GAN, there is a generator and a discriminator. In this context, the generator is the LLM that RLMF seeks to improve, while the discriminator acts as a tester. In a GAN, the discriminator distinguishes between real data (from the training set) and generated data (from the generator). In RLMF, the discriminatorβs role is adapted to LLMs, providing feedback on the quality of the outputs, which allows the generator to update its parameters and improve over time.
I. State of the Art (SOTA)
Recent works have explored similar approaches:
Google: Training Language Models to Self-Correct via Reinforcement Learning (SCoRe) is an innovative multi-turn, online reinforcement learning method that enhances LLMs’ self-correction abilities using entirely self-generated data (Kumar et al., 2024).
- Limitation: This approach lacks the ability to verify responses against external data, relying solely on internal feedback without objective validation.
Meta: Mixture of Judges (MoJ) is a multi-layered feedback system within the CGPO framework. It employs both rule-based and LLM-based judges to evaluate and optimize outputs in multi-task learning, preventing reward hacking and managing conflicting objectives (Xu et al., 2024).
- Limitation: This system relies on subjective internal evaluations from LLM-based judges, which may not always ensure factual correctness when compared to objective external validation systems.
Both methods rely heavily on internal data, akin to pupils grading their own exams. While intriguing, this approach may introduce bias and lacks the objectivity that external validation could provide, making it a potentially flawed method for assessment.
II. How Does RLMF Work?
- External Knowledge with RAG: The LLM Discriminator generates random user prompts and uses external Retrieval-Augmented Generation (RAG) to source real-world data relevant to the questions.
- Answer Evaluation: The system evaluates LLM responses for factual correctness. If correct, a new prompt is generated.
- Error Handling: Incorrect answers or hallucinations are flagged by comparing LLM responses with RAG-sourced data.
- Penalties and Dataset Creation: Errors trigger penalties via reinforcement learning, and incorrect entries are logged for Direct Preference Optimization (DPO).
- Continuous Fine-Tuning: The model is continuously fine-tuned in a feedback loop, ensuring it adapts and improves over time.
As this technology evolves, it promises to unlock even more potential in the field of language model optimization by integrating external knowledge sources and refining its ability to self-correct.
Stay tuned for the final episode next week.
#SCoRe #Mixture_of_Judges #RLMF