arXiv:2605.24743v1 Announce Type: new Abstract: While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its performance hinges on the availability and quality of multi-turn trajectory data. A common remedy is to augment training with synthetic trajectories generated by LLMs or simulators, but synthetic data is highly heterogeneous in quality, and naively treating all trajectories as equally informative can degrade performance. We propose BOOST, a bilevel optimization framework wher
The rapid advancement of large language models (LLMs) has exposed limitations in multi-turn interactions, making the development of robust fine-tuning methods a critical next step for their mature application.
Improving LLM performance in complex, multi-turn interactions is crucial for developing more sophisticated and reliable AI agents and applications across many industries.
The ability to effectively fine-tune LLMs using synthetic data for multi-turn interactions significantly expands the practical applicability of these models, especially where real-world data is scarce.
- · AI developers
- · Enterprises deploying LLMs for complex tasks
- · SaaS platforms leveraging LLMs for customer interaction
- · Companies relying on single-turn LLM capabilities for complex interactions
LLMs become significantly more capable in sustained dialogues and complex task execution.
This improved capability accelerates the deployment of sophisticated AI agents in various sectors, automating more complex workflows.
The enhanced performance of AI agents could lead to new business models and services, while displacing human workers in certain knowledge-intensive roles.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG