Reinforcement
ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks
LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific…
New tool evaluates progress in reinforcement learning
If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. …
SQL-R1: A Reinforcement Learning-based NL2SQL Model that Outperforms Larger Systems in Complex Queries with Transparent and Accurate SQL Generation
Natural language interface to databases is a growing focus within artificial intelligence, particularly because it allows users to interact with structured databases using plain human language. This area, often known as NL2SQL (Natural Language to SQL), is centered on transforming user-friendly queries into SQL commands that can be directly executed on databases. The objective is…
Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment
Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient…
Reinforcement Learning Meets Chain-of-Thought: Transforming LLMs into Autonomous Reasoning Agents
Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in logical reasoning remains a challenge. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning. This limits their ability to solve complex problems…
This AI Paper Explores Long Chain-of-Thought Reasoning: Enhancing Large Language Models with Reinforcement Learning and Supervised Fine-Tuning
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,…
Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning Capabilities
Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get…
Google DeepMind Introduces MONA: A Novel Machine Learning Framework to Mitigate Multi-Step Reward Hacking in Reinforcement Learning
Reinforcement learning (RL) focuses on enabling agents to learn optimal behaviors through reward-based training mechanisms. These methods have empowered systems to tackle increasingly complex tasks, from mastering games to addressing real-world problems. However, as the complexity of these tasks increases, so does the potential for agents to exploit reward systems in unintended ways, creating new…