Deep Reinforcement Learning of LLM s​ using RLHF (2025)

Abstract: My main research interests lies in the application of Reinforcement Learning (RL) alignment of LLMs in human robot dialogue. More specifically, my latest research aims to use RL alignment as an efficient training regime to train a newly initialized tiny LM to behave like a toddler. Previous research expresses the difficulty of building a robust tiny LM with an educated adult level understanding. Our hypothesis is that the cognitive barrier to train a tiny LM to at-least behave as a child is achievable with a very small number of parameters especially if training efficiently using RL LLM training regime. My interests also extend to apply RL to LLM training for dialogue management and planning.

## research interests my main research interests lies in the application of reinforcement learning (rl) alignment of llms in human robot dialogue. more specifically, my latest research aims to use rl alignment as an efficient training regime to train a newly initialized tiny lm to behave like a toddler. previous research expresses the difficulty of building a robust tiny lm with an educated adult level understanding. our hypothesis is that the cognitive barrier to train a tiny lm to at-least behave as a child is achievable with a very small number of parameters especially if training efficiently using rl llm training regime. my interests also extend to apply rl to llm training for dialogue management and planning. ## spoken dialogue system (sds) research i think the progress of sds has dramatically been driven forward since the rise of llms and has lots of potential of where it can go. for the most part this is because we have alot of text on the internet in which we can process easily unlike other domains. however, i believe the amount of speech and visual data is also within the domain. as for the future of research, firstly, i think there will be significant progress in multi-model llms allowing sds to encompass visual and speech integration much more fluently. secondly, i think speech and audio will be embedded into a transformer model allows for llms to natively conduct stt and emotion recognition taking a step forward in llms behaving more closely to humans. industrial research is a lot more financial and goal driven, but only selects research on areas where shortterm roi can be relatively guaranteed such as an agentic airline agent. where as academia research is rather driven by the interest of younger thinkers. thus the future of sds has a lot to do with how the younger generation is introduced to sds and how they envisions its future. i think students envision mimicking a broader human to human interactions and environmental interaction rather then simpler agentic airline bots. thus i think sds research will be financially driven in areas more where the industry can see a short-term roi in areas such as stronger llms, stt, tts, and vision models. in this section, the authors will briefly present their opinions and point of views of current and future sdss research in general. these could include answers to questions like: ## suggested topics for discussion here, authors will suggest three topics for discussion in the discussion panels during the event. as an example, here are some of the discussion topics discussed in previous workshops: