The hottest Reinforcement Learning Substack posts right now

And their main takeaways

Bullet Points: A couple predictions for AI in 2024

The Future, Now and Then • 146 implied HN points • 01 Jan 24

The AI industry might face challenges regarding copyright laws like the Ghost of Napster did.
Generative AI could turn out to be a significant upgrade for existing machine learning systems.
The impact of AI in 2024 may largely build upon where machine learning was already established.

Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23

🕹 Technology AI Machine Learning Neural Networks Reinforcement Learning Language Models

Reinforcement Learning is a technique that helps models learn from experiencing pleasure and pain in their environment over time.
Human feedback plays a crucial role in fine-tuning language models by providing ratings that indicate how a model's output impacts users' feelings.
To train models effectively, a preference model can be used to emulate human responses and provide feedback without the need for extensive human involvement.

Q*, Reinforcement Learning and Search

Yuxi’s Substack • 58 implied HN points • 24 Nov 23

🕹 Technology AI Reinforcement Learning Search Language Models Machine Learning

Q* represents the optimal Q value in reinforcement learning integrating learning and search.
Reinforcement learning helps an agent learn a policy to maximize long-term rewards through interactions with the environment.
RL for LLMs combines learning and search techniques for next-generation language models.

Must Learn AI Security Part 12: Reward Hacking Attacks Against AI

Rod’s Blog • 59 implied HN points • 13 Sep 23

🕹 Technology AI Security Reinforcement Learning

Reward Hacking attacks against AI involve AI systems exploiting flaws in reward functions to gain more rewards without achieving the intended goal.
Types of Reward Hacking attacks include gaming the reward function, shortcut exploitation, reward tampering, negative side effects, and wireheading.
Mitigating Reward Hacking involves designing robust reward functions, monitoring AI behavior, incorporating human oversight, and using techniques like adversarial training and model-based reinforcement learning.

We Aren't Close To Creating A Rapidly Self-Improving AI

As Clay Awakens • 129 HN points • 26 Apr 23

🕹 Technology AI Deep Learning Data Collection Generalization Reinforcement Learning

Creating an AI that rapidly self-improves still needs a paradigm-changing breakthrough.
Current AI methods can reach human-level performance on various tasks with enough data.
Automatically constructing high-quality datasets for AI training is a challenging problem yet to be solved.

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Will synthetic data help?

Yuxi’s Substack • 19 implied HN points • 24 Nov 23

🕹 Technology Language Models Training Data Reinforcement Learning

A perfect model can create high-quality data to build strong AI, like AlphaZero - AIZero
Without a perfect model, gathering high-quality data is essential for competent AI - AI∞ or AIx
It is important to start AI systems with ground truth data and work towards bridging the gap between simulation and reality

Quant Letter: October 2023, Week 2

The Parlour • 21 implied HN points • 12 Oct 23

💰 Finance Quantitative finance Machine Learning Reinforcement Learning

The post is about a quantitative finance newsletter for October 2023, Week 2.
A recently published thesis discusses Deep RL for Portfolio Allocation, showing the potential of deep reinforcement learning in enhancing portfolio allocation methods.
Readers can subscribe to Machine Learning & Quant Finance for more content and a 7-day free trial.

Human alignment is very hard

Yuxi’s Substack • 19 implied HN points • 04 Sep 23

🔬 Science Ethics Optimization Human feedback Reinforcement Learning

Human alignment is very challenging and complex.
Human alignment involves multiple facets and perspectives.
Balancing trade-offs among various factors is crucial in addressing the human alignment problem.

Playing Chess - LLMs and Actual Chess AIs

Age of AI • 19 implied HN points • 04 Jul 23

🕹 Technology AI Chess Machine Learning Reinforcement Learning

Large Language Models like ChatGPT can learn strategy games but won't reach top chess AI levels.
True Chess AI like AlphaZero and MuZero outperform traditional chess programs by learning through reinforcement.
Human-level chess AI like Maia Chess is designed to play like humans, predicting moves without looking ahead.

AI Stores

Yuxi’s Substack • 19 implied HN points • 15 Feb 23

🕹 Technology AI Machine Learning Deep Learning Reinforcement Learning Artificial General Intelligence

We are entering the era of AI Stores.
An AI Store provides general AI capabilities like drafting emails, drawing, and suggesting software code.
Contributing to or benefiting from AI Stores can range from being a customer to fine-tuning models based on resources.

Where is the boundary for large language models?

Yuxi’s Substack • 19 implied HN points • 12 Mar 23

🕹 Technology AI Language Models Ethics Reinforcement Learning

The boundary for large language models involves considerations of grounding, embodiment, and social interaction.
Language models are transitioning towards incorporating agency and reinforcement learning methods for better performance.
AI Stores may potentially lead to AI models providers encroaching on the territories of downstream model users.

Helpful and unhelpful anthropomorphism

Apperceptive (moved to buttondown) • 6 implied HN points • 26 Jul 23

🕹 Technology AI ML Neural Networks Reinforcement Learning

Anthropomorphism can be both helpful and unhelpful when understanding ML systems like LLMs.
LLMs are trained through autoregressive next word prediction and reinforcement learning.
LLMs do not have the same complex internal states or motivations as humans, despite appearing human-like in their responses.

RL(HF) Helps LMs

Yuxi’s Substack • 0 implied HN points • 23 Jul 23

🕹 Technology AI Language Models Reinforcement Learning Human feedback

Reinforcement learning from human feedback helps with human value alignment in language models.
Direct Preference Optimization (DPO) can optimize preference directly without using reward modeling or reinforcement learning.
There are various methods, like TAMER, to handle human preference and alignment in language models beyond DPO.

How would Deepmind Gemini work?

Yuxi’s Substack • 0 implied HN points • 08 Nov 23

🕹 Technology AI Machine Learning Deep Learning Robotics Reinforcement Learning

Deepmind is working on multimodality, embodiment, and interaction in addition to language models.
Iterative improvements from feedback are crucial for building successful systems and bridging gaps.
Deepmind is exploring deep reinforcement learning in language models, but its deployment in Gemini is uncertain.

Study Material for Reinforcement Learning

Yuxi’s Substack • 0 implied HN points • 24 Nov 23

🚌 Education Reinforcement Learning

Key resources for studying Reinforcement Learning include classic courses by David Silver and textbooks by Sutton & Barto
Online platforms like OpenAI Spinning Up and Coursera offer specialized courses on Reinforcement Learning
Advanced resources like DeepMind's lecture series and UC Berkeley's Deep RL course provide in-depth knowledge on the subject