Reflections on Pretraining and Fine-Tuning in Reinforcement Learning

The world of reinforcement learning (RL) is continuously advancing and a recent study, titled "Reflections on Pretraining and Fine-Tuning in Reinforcement Learning" (source), further emphasizes the significance of pretraining. The authors surprisingly don't discuss open sourcing the weights, raising questions about their stance on knowledge sharing.

The study suggests that constructing a high-quality dataset for instruction fine-tuning could outshine larger, but less balanced datasets. This process could be optimized through a crowdsourcing approach to lessen prompt author overlap, thereby increasing the diversity of phrasing. The authors argue that with sufficient quality, you could develop an instruction model that excels at role-playing, storytelling, or other specific tasks.

The process the authors used involved training with prompts that described how they intended to solve a problem, followed by the actual solution. They speculate that the improvement comes from a type of step-by-step reasoning process. However, this approach could be equally effective for story writing prompts that include an initial planning explanation, with the main text being the visible output.

While the name of the study might be slightly misleading, its core idea focuses on the notion that the final fine-tuning of the Large Language Model (LLM) to transform into a chatbot forms a lesser part of the overall training. In essence, the optimal way to train an LLM is to expend most resources on the encoder and decoder language training, with only a small percentage focused on the final instruction fine-tuning. This concept aligns with other research in the field, but serves as a beneficial reminder.

Overall, the paper is an intriguing read and is reminiscent of early experiments with base llama acting as an assistant. It fosters hope for more experiments using this approach, leading to further advancement in reinforcement learning.

Tags: Reinforcement Learning, Pretraining, Fine Tuning,LLM

Improving Llama.cpp Model Output for Agent Environment with WizardLM and Mixed-Quantization Models

Llama.cpp is a powerful tool for generating natural language responses in an agent environment. One way to speed up the generation process is to save the prompt ingestion stage to cache using the --session parameter and giving each prompt its own session name. Furthermore, using the impressive and fast WizardLM 7b (q5_1) and comparing its results with other new fine tunes like TheBloke/wizard-vicuna-13B-GGML could also be useful, especially when prompt-tuning. Additionally, adding the llama.cpp parameter --mirostat has been … click here to read

Re-Pre-Training Language Models for Low-Resource Languages

Language models are initially pre-trained on a huge corpus of mostly-unfiltered text in the target languages, then they are made into ChatLLMs by fine-tuning on a prompt dataset. The pre-training is the most expensive part by far, and if existing LLMs can't do basic sentences in your language, then one needs to start from that point by finding/scraping/making a huge dataset. One can exhaustively go through every available LLM and check its language abilities before investing in re-pre-training. There are surprisingly many of them … click here to read

Automated Reasoning with Language Models

Automated reasoning with language models is a fascinating field that can test reasoning skills. Recently, a model named Supercot showed accidental proficiency in prose/story creation. However, it's essential to use original riddles or modify existing ones to ensure that the models are reasoning and not merely spewing out existing knowledge on the web.

Several models have been tested in a series of reasoning tasks, and Vicuna-1.1-Free-V4.3-13B-ggml-q5_1 has been tested among others. It performed well, except for two coding points. Koala performed slightly better … click here to read

Engaging with AI: Harnessing the Power of GPT-4

As Artificial Intelligence (AI) becomes increasingly sophisticated, it’s fascinating to explore the potential that cutting-edge models such as GPT-4 offer. This version of OpenAI's Generative Pretrained Transformer surpasses its predecessor, GPT-3.5, in addressing complex problems and providing well-articulated solutions.

Consider a scenario where multiple experts - each possessing unique skills and insights - collaborate to solve a problem. Now imagine that these "experts" are facets of the same AI, working synchronously to tackle a hypothetical … click here to read

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

In the ever-evolving landscape of deep learning, a new contender has emerged – Mamba. This linear-time sequence modeling approach is causing quite a stir in the community, promising efficient computation and groundbreaking results.

Some have speculated that Mamba could be the game-changer, while others were skeptical, citing comparisons with well-established transformers.

For those unfamiliar with Mamba, a detailed exploration and practical experiment insights … click here to read

Decoding AWQ: A New Dimension in AI Model Efficiency

It seems that advancements in artificial intelligence are ceaseless, as proven by a new methodology in AI model quantization that promises superior efficiency. This technique, known as Activation-aware Weight Quantization (AWQ), revolves around the realization that only around 1% of a model's weights make significant contributions to its performance. By focusing on these critical weights, AWQ achieves compelling results.

In simpler terms, AWQ deals with the observation that not all weights in Large Language Models (LLMs) are equally important. … click here to read

Open Source Projects: Hyena Hierarchy, Griptape, and TruthGPT

Hyena Hierarchy is a new subquadratic-time layer in AI that combines long convolutions and gating, reducing compute requirements significantly. This technology has the potential to increase context length in sequence models, making them faster and more efficient. It could pave the way for revolutionary models like GPT4 that could run much faster and use 100x less compute, leading to exponential improvements in speed and performance. Check out Hyena on GitHub for more information.

Elon Musk has been building his own … click here to read

Reimagining Language Models with Minimalist Approach

The recent surge in interest for smaller language models is a testament to the idea that size isn't everything when it comes to intelligence. Models today are often filled with a plethora of information, but what if we minimized this to create a model that only understands and writes in a single language, yet knows little about the world? This concept is the foundation of the new wave of "tiny" language models .

A novel … click here to read

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