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Found 6 papers, 3 papers with code
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models
The idea is to learn a simple linear function on a model's embedding space that can be used to reweight candidate completions.
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization
We propose ABCs (Adaptive Branching through Child stationarity), a best-of-both-worlds algorithm combining Boltzmann Q-learning (BQL), a classic reinforcement learning algorithm for single-agent domains, and counterfactual regret minimization (CFR), a central algorithm for learning in multi-agent domains.
Chain-of-Thought Reasoning is a Policy Improvement Operator
We introduce SECToR (Self-Education via Chain-of-Thought Reasoning), a proof-of-concept demonstration that language models can teach themselves new skills using chain-of-thought reasoning.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
Trading Off Diversity and Quality in Natural Language Generation
For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity.
Unifying Human and Statistical Evaluation for Natural Language Generation
How can we measure whether a natural language generation system produces both high quality and diverse outputs?
