Filtered to language models · clear filter
This paper proposes a new optimization technique for long-horizon language model agents, allowing them to make more informed decisions by allocating credit more finely across individual tokens. Practitioners might care because it could lead to better performance in tasks like search, tool-use, and automation.
This paper discovers that language models can learn from unrelated text after being trained on a specific task, and introduces a method called Active Taskless Distillation (ATD) that allows this capability transfer to happen without using target-task examples or teacher parameters. Practitioners might care because it could lead to more efficient and effective language model training.
This paper teaches language agents to reflect on their actions and adapt to changing environments, allowing them to learn from feedback and correct mistakes more effectively. Practitioners can benefit from this approach in real-world applications where agents need to handle noisy feedback and user corrections.
This paper proposes a new method for managing the memory of large language models, called Random Attention, which improves their performance on tasks that require extended reasoning by reducing memory usage. Practitioners caring about efficient model deployment might find this approach useful.
This paper develops a new type of AI model designed to help with teaching and learning in schools, and it shows that such models can be very effective at solving educational problems.
This paper creates a platform to help large language models generate code for data pipelines, which can then be edited and used to automate data processing workflows. Practitioners might care about this because it can help reduce the time and cost of developing and maintaining these pipelines.
This paper introduces LatentPress, a method to compress conversational histories and documents into a continuous memory token format that allows language models to directly read and process the context without text reconstruction. Practitioners might care about this because it could lead to faster and more efficient language model inference.
This paper introduces Co-RL, a framework for unsupervised multi-agent reinforcement learning that enables diverse and accurate reasoning in language and vision-language models. Practitioners can use Co-RL to improve their models' ability to reason and respond without relying on expensive ground-truth supervision.
This paper proposes a new method for fine-tuning large language models (LLMs) in reinforcement learning (RL) tasks with long horizons, using evolution strategies (ES) instead of traditional backpropagation-based training. Practitioners might care because it allows for more efficient and flexible fine-tuning of LLMs, enabling them to tackle complex tasks with larger models and longer interactions.