10 accepted papers
In recent years, with the rapid development of large language models (LLMs), LLM-based agents have achieved remarkable progress across a wide range of tasks. However, reasoning inconsistencies in LLMs still significantly limit the performance of agents in complex decision-making scenarios. Cognitive
Many studies focus on data annotation techniques for training effective PRMs. However, current methods encounter a significant issue when applied to long CoT reasoning processes: they tend to focus solely on the first incorrect step and all preceding steps, assuming that all subsequent steps are inc
In recent years, diversity has emerged as a useful mechanism to enhance the efficiency of multi-agent reinforcement learning (MARL). However, existing methods predominantly focus on designing policies based on individual agent characteristics, often neglecting the interplay and mutual influence amon
Parameter sharing is a common strategy in multi-agent reinforcement learning (MARL) to make the training more efficient and scalable. However, applying parameter sharing among agents indiscriminately hinders the emergence of agents diversity and degrades the final cooperative performance. To better…
Distributed deep reinforcement learning (DDRL) has gained increasing attention due to the emerging requirements for addressing complex games like Go and StarCraft. However, how to effectively and stably train bots with asynchronous and heterogeneous agents cooperation and competition for multiple pl…
Despite the dramatic success in image generation, Generative Adversarial Networks (GANs) still face great challenges in text generation. The difficulty in generator training arises from the limited representation capacity and uninformative learning signals obtained from the discriminator. In this wo…
Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. One possible solution to this issue is to exploit inherent task structures for an acceleration of exploration. In this paper, we present a novel exploration approach, which encodes a special structur…
Natural language generation (NLG) is an important task with various applications like neural machine translation (NMT) and image captioning. Since deep-learning-based methods have issues of exposure bias and loss inconsistency, reinforcement learning (RL) is widely adopted in NLG tasks recently. But…
Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language. Challenges arise from the uninformative learning signals passed from the discriminator. In other words, the poor learning signals limit the learning ca…
Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories may be deleterious for training the action-value and policy functions. To allevi…