Papers with policy network
Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)
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| Challenge: | End-to-end neural machine translation (NMT) has attracted increasing attention in recent years. |
| Approach: | They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning. |
| Outcome: | The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism . |
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems (2020.findings-emnlp)
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| Challenge: | In order to improve the sample-efficiency of deep reinforcement learning, we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). |
| Approach: | They propose to use an actor-double-critic to improve the stability and overall performance of imagination augmented agent (I2A) in spoken dialogue systems. |
| Outcome: | The proposed model-based agent (ADC) improves the stability and sample-efficiency of deep reinforcement learning (DRL) on a restaurant booking task. |
Struct-XLM: A Structure Discovery Multilingual Language Model for Enhancing Cross-lingual Transfer through Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Existing methods require syntactic labels that are difficult to obtain and of poor quality for low-resource languages. |
| Approach: | They propose a syntactic alignment model that leverages reinforcement learning to discover universal syntaktic structures for cross-lingual PLM alignment. |
| Outcome: | The proposed model improves cross-lingual representation alignment on the XTREME benchmark. |
End-to-End Reinforcement Learning for Automatic Taxonomy Induction (P18-1)
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| Challenge: | Existing methods for automating taxonomy induction often divide the problem into two subtasks . a novel end-to-end reinforcement learning approach is proposed to improve the accuracy of such methods. |
| Approach: | They propose an end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets of different domains. |
Inverse Reinforcement Learning for Text Summarization (2023.findings-emnlp)
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| Challenge: | Existing studies show that inverse reinforcement learning (RL) training has certain disadvantages such as object mismatch and exposure bias. |
| Approach: | They propose inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models. |
| Outcome: | The proposed model outperforms MLE and RL baselines on ROUGE, coverage, novelty, compression ratio, factuality, and human evaluations. |
Multi-Label Few-Shot Learning for Aspect Category Detection (2021.acl-long)
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| Challenge: | Existing few-shot learning methods focus on single-label predictions, which can not work well for ACD since a sentence may contain multiple aspect categories. |
| Approach: | They propose a few-shot learning method that uses the prototypical network to learn aspects from a set of aspects. |
| Outcome: | The proposed method significantly outperforms baseline methods on three datasets. |
FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)
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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets. |
| Approach: | They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization. |
| Outcome: | The proposed framework improves performance in trading and other financial domain tasks. |
LeLoRA: Learnable Low-Rank Adaptation of Large Language Models (2026.acl-long)
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| Challenge: | Existing approaches to fine-tuning large language models (LLMs) rely on manually specified and fixed hyperparameters, resulting in suboptimal performance and low parameter efficiency. |
| Approach: | They propose a framework that allows for dynamically learned adaptive adaptation strategies to be used to fine-tune large language models. |
| Outcome: | The proposed framework outperforms baselines in adapting large language models. |