InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training
arXiv:2510.15859v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has powered many recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be computed automatically, such as code generation. However, it is less effective in open-ended medical dialogue, where feedback is ambiguous, context-dependent, and difficult to simply summarize into a single scalar signal-often requiring heavily supervised reward models and creating risks of reward hacking. Thus, we introduce ORBIT, an open-ended rubric-based incremental training framework tailored
The increasing complexity of open-ended tasks for LLMs, especially in critical domains like medicine, necessitates more robust alignment mechanisms beyond traditional RL, driving innovation in training methodologies.
This development addresses a fundamental limitation in LLM application for sensitive, open-ended tasks, potentially unlocking significant advancements in intelligent systems beyond current capabilities.
The introduction of ORBIT signifies a shift in how LLMs for complex, ambiguous domains are trained and aligned, moving towards rubric-based incremental methods over scalar rewards.
- · AI developers
- · Healthcare sector
- · Patients
- · LLM applications in complex domains
- · Traditional RL-based alignment methods
- · LLMs with poor open-ended task performance
Improved performance and reliability of LLMs in open-ended medical dialogue and similar complex tasks.
Accelerated adoption of LLMs in highly sensitive and regulated industries due to enhanced trust and safety.
New ethical considerations and regulatory frameworks emerging from the deployment of highly autonomous and aligned medical AI agents.
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Read at arXiv cs.AI