51 citations · 83 across the 14 of their papers we have counts for
14 papers
Composer 2 Technical Report
Cursor Research, :, Aaron Chan +53
Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability…
Sleep-time Compute: Beyond Inference Scaling at Test-time
Kevin Lin, Charlie Snell, Yu Wang +4
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We…
Reasoning Models Can Be Effective Without Thinking
Wenjie Ma, Jingxuan He, Charlie Snell +3
Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question wheth…
Learning Adaptive Parallel Reasoning with Language Models
Jiayi Pan, Xiuyu Li, Long Lian +6
Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chai…
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
Violet Xiang, Charlie Snell, Kanishk Gandhi +11
We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive…
Predicting Emergent Capabilities by Finetuning
Charlie Snell, Eric Wallace, Dan Klein +1
A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, language model pretraining loss is known to be highly p…
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Marwa Abdulhai, Isadora White, Charlie Snell +5
Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed age…
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande, Eric Wallace, Charlie Snell +5
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Inst…
Learning by Distilling Context
Charlie Snell, Dan Klein, Ruiqi Zhong
Language models significantly benefit from context tokens, such as prompts or scratchpads. They perform better when prompted with informative instructions, and they acquire new rea…
Offline RL for Natural Language Generation with Implicit Language Q Learning
Charlie Snell, Ilya Kostrikov, Yi Su +2
Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by…
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL
Ruiqi Zhong, Charlie Snell, Dan Klein +1
Can non-programmers annotate natural language utterances with complex programs that represent their meaning? We introduce APEL, a framework in which non-programmers select among ca…
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Charlie Snell, Mengjiao Yang, Justin Fu +2
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of pro…
Describing Differences between Text Distributions with Natural Language
Ruiqi Zhong, Charlie Snell, Dan Klein +1
How do two distributions of texts differ? Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples. We propose to a…
Approximating How Single Head Attention Learns
Charlie Snell, Ruiqi Zhong, Dan Klein +1
Why do models often attend to salient words, and how does this evolve throughout training? We approximate model training as a two stage process: early on in training when the atten…