As artificial intelligence (AI) systems surpass human capabilities in various tasks, ensuring alignment with human values and ethics is crucial. This is especially important for large language models (LLMs), which are trained on diverse datasets that may contain harmful content. Reinforcement Learning from Human Feedback (RLHF) is an effective method for AI alignment, with models like OpenAI's GPT-4, Google's Gemini, and Anthropic Claude showing safe and aligned behaviors. However, the vast majority of the current research in RLHF (Agarwal et al., 2020; Rafailov et al., 2023; Ouyang et al., 2022; Chakraborty et al., 2024; Swamy et al., 2024) focuses on the offline setting, which involves using a fixed dataset of responses generated by the supervised fine-tuned model (SFT), ranked by human experts. Consequently, these methods are inherently offline and heavily reliant on the quality of the offline data generated by the SFT model, which exhibits drawbacks such as insufficient coverage of response-query pairs leading to sub-optimal alignment. To deal with the above shortcomings, recent literature (Guo et al., 2024a; Sharma et al., 2024; Lee et al., 2023; Yuan et al., 2024b) has focused on designing online RLHF algorithms. The setting of online RLHF transcends the constraints of a static offline dataset and aims to address two critical questions: Q1: How should we generate new responses during fine-tuning?

Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems. The problem gets even more pronounced in the case of low resource languages such as Hindi. In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi. Documents are cleaned and structured to enable the development of downstream applications. Further, as a use-case for the corpus, we introduce the task of bail prediction. We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same. MTL models use summarization as an auxiliary task along with bail prediction as the main task. Experiments with different models are indicative of the need for further research in this area. We release the corpus and model implementation code with this paper: https://github.com/Exploration-Lab/HLDC