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Found 10 papers, 5 papers with code
How Far Are We from Optimal Reasoning Efficiency?
To reduce the efficiency gap, we propose REO-RL, a class of Reinforcement Learning algorithms that minimizes REG by targeting a sparse set of token budgets.
AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
Most existing large-scale RL systems for LLMs are synchronous, alternating generation and training in a batch setting where rollouts in each training batch are generated by the same model.
On Designing Effective RL Reward at Training Time for LLM Reasoning
In this work, we evaluate popular reward models for RL training, including the Outcome-supervised Reward Model (ORM) and the Process-supervised Reward Model (PRM), and train a collection of LLMs for math problems using RL by combining these learned rewards with success rewards.
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
However, in academic benchmarks, state-of-the-art results are often achieved via reward-free methods, such as Direct Preference Optimization (DPO).
A Benchmark for Low-Switching-Cost Reinforcement Learning
A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.
PhyloTransformer: A Discriminative Model for Mutation Prediction Based on a Multi-head Self-attention Mechanism
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic infecting 219 million people as of 10/19/21, with a 3. 6% mortality rate.
Sequence Level Contrastive Learning for Text Summarization
In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training.
Deep Q-Learning with Low Switching Cost
We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
We also find in experiments that our model is less dependent on sentence positions.
