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Found 14 papers, 7 papers with code
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
LongWanjuan: Towards Systematic Measurement for Long Text Quality
The quality of training data are crucial for enhancing the long-text capabilities of foundation models.
Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation
To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world during training and 2) look for the target object based on the pre-learned object category relations and its moving trajectory in the current unseen environment.
A Reliable Representation with Bidirectional Transition Model for Visual Reinforcement Learning Generalization
Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations.
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo.
AdaLomo: Low-memory Optimization with Adaptive Learning Rate
Building on this insight, we introduce the low-memory optimization with adaptive learning rate (AdaLomo), which offers an adaptive learning rate for each parameter.
Full Parameter Fine-tuning for Large Language Models with Limited Resources
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training.
Unified Demonstration Retriever for In-Context Learning
To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback.
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning
We firstly define and describe the heterogeneous problems in SMAC.
CoNT: Contrastive Neural Text Generation
We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation.
FN-Net:Remove the Outliers by Filtering the Noise
When estimating the relationship between two images, it is often disturbed by outliers.
Conservative Distributional Reinforcement Learning with Safety Constraints
Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.
Distributional Reinforcement Learning
reinforcement-learning
+1
A region-based descriptor network for uniformly sampled keypoints
By the new descriptor, we can obtain more high confidence matching points without extremum operation.
