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Found 6 papers, 5 papers with code
Prover-Verifier Games improve legibility of LLM outputs
One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check -- a property we call legibility.
Let's Verify Step by Step
We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset.
Ranked #1 on
Math Word Problem Solving
on MATH minival
(using extra training data)
AutoDIME: Automatic Design of Interesting Multi-Agent Environments
One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents.
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
In this paper we propose to study generalization of neural networks on small algorithmically generated datasets.
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
Large-Scale Study of Curiosity-Driven Learning
However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent.
Ranked #14 on
Atari Games
on Atari 2600 Montezuma's Revenge
