Papers with reinforcement learning techniques
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models (2025.emnlp-demos)
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| Challenge: | In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models. |
| Approach: | They propose a toolkit for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). |
| Outcome: | The framework offers data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios. |
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to train simultaneous machine translation agents have been used to find the optimal action sequences for translation quality and lag. |
| Approach: | They propose a supervised learning approach that detects minimum reads required for generating target tokens by comparing simultaneous translations against full-sentence translations. |
| Outcome: | The proposed method produces much higher quality translations while minimizing the average lag in simultaneous translation. |
Reinforcement Learning for Topic Models (2023.findings-acl)
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| Challenge: | Topic modeling is a method to extract information from documents by grouping topics into topics and linking them with words describing them. |
| Approach: | They propose to replace the variational autoencoder with a continuous action space reinforcement learning policy and modify the neural network architecture to weight the ELBO loss. |
| Outcome: | The proposed model outperforms all other unsupervised models and performs on par with or better than most models using supervised labeling and contrastive learning. |
QueStER: Query Specification for Generative Keyword-Based Retrieval (2026.findings-eacl)
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| Challenge: | Generative retrieval (GR) models can be expensive and brittle out of domain. |
| Approach: | They propose a query specification for gEnerative Keyword-Based Retrieval which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. |
| Outcome: | The proposed query specification improves over existing queries and maintains strong efficiency. |
Goal-oriented Vision-and-Dialog Navigation via Reinforcement Learning (2022.findings-emnlp)
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| Challenge: | Existing methods for vision-and-dialog navigation are limited and do not adapt to domain changes. |
| Approach: | They propose a problem where an agent computes dialog-navigation policies from trial and error. |
| Outcome: | The proposed agent outperforms baselines in success rate in photo-realistic simulations. |
AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)
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| Challenge: | Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated. |
| Approach: | They propose to use a context-generator to generate travel and flight restrictions to train agents. |
| Outcome: | The proposed model achieves a score of 0.17 while humans can reach 0.91 . the proposed model is based on a large dataset that contains 301,427 goal-oriented conversations . |
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning (D18-1)
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| Challenge: | Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. |
| Approach: | They propose a method which learns from users’ preferences instead of reference summaries by interacting with the oracle for multiple rounds and leveraging active learning, preference learning and reinforcement learning techniques. |
| Outcome: | The proposed method significantly advances the state of the art in both simulation and real-user experiments. |
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. |
| Approach: | They propose a new compression paradigm that extracts knowledge from pre-trained language models to construct a knowledge store from which the model can leverage it for effective inference. |
| Outcome: | The proposed model extracts knowledge from LLMs to construct a knowledge store, which the model can leverage for effective inference. |
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (2024.emnlp-main)
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| Challenge: | Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage. |
| Approach: | They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy. |
| Outcome: | The proposed approach scales up the diversity of test cases using a top-down approach based on an extensible, fine-grained risk taxonomy and leverages reinforcement learning techniques to facilitate multi-turn adversarial probing in a human-like manner. |
ABLE: Personalized Disability Support with Politeness and Empathy Integration (2024.emnlp-main)
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| Challenge: | Adaptive, Bespoke, Listen and Empathetic is a conversational support system for physical disabilities that tracks user personas and provides personalized support according to user person preferences. |
| Approach: | They propose a conversational support system that tracks user personas and integrates politeness and empathy levels into responses to ensure that support interactions are tailored to each user's characteristics and preferences. |
| Outcome: | The proposed system is based on a conversational dataset enriched with user profile annotations and tested on 84 users with physical disabilities. |