Optimizing Life Sciences Agents in Real-Time using Reinforcement Learning

• URL: http://arxiv.org/abs/2512.03065v1
• Date: Wed, 26 Nov 2025 16:05:02 GMT
• Title: Optimizing Life Sciences Agents in Real-Time using Reinforcement Learning
• Authors: Nihir Chadderwala,
• Abstract summary: We present a novel framework that combines AWS Strands Agents with Thompson Sampling contextual bandits to enable AI agents to learn optimal decision-making strategies from user feedback alone.<n>We demonstrate 15-30% improvement in user satisfaction compared to random baselines, with clear learning patterns emerging after 20-30 queries.<n>Our approach requires no ground truth labels, adapts continuously to user preferences, and provides a principled solution to the exploration-exploitation dilemma in agentic AI systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generative AI agents in life sciences face a critical challenge: determining the optimal approach for diverse queries ranging from simple factoid questions to complex mechanistic reasoning. Traditional methods rely on fixed rules or expensive labeled training data, neither of which adapts to changing conditions or user preferences. We present a novel framework that combines AWS Strands Agents with Thompson Sampling contextual bandits to enable AI agents to learn optimal decision-making strategies from user feedback alone. Our system optimizes three key dimensions: generation strategy selection (direct vs. chain-of-thought), tool selection (literature search, drug databases, etc.), and domain routing (pharmacology, molecular biology, clinical specialists). Through empirical evaluation on life science queries, we demonstrate 15-30\% improvement in user satisfaction compared to random baselines, with clear learning patterns emerging after 20-30 queries. Our approach requires no ground truth labels, adapts continuously to user preferences, and provides a principled solution to the exploration-exploitation dilemma in agentic AI systems.

Related papers

• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization [69.36509281190662]
  Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck.<n>We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design.<n>We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions.
  arXiv  Detail & Related papers  (2025-12-02T18:42:26Z)
• Aligning Machiavellian Agents: Behavior Steering via Test-Time Policy Shaping [5.161558858101654]
  We propose a test-time alignment technique based on model-guided policy shaping.<n>Our method allows precise control over individual behavioral attributes, generalizes across diverse reinforcement learning environments.<n>Our results demonstrate that test-time policy shaping provides an effective and scalable solution for mitigating unethical behavior.
  arXiv  Detail & Related papers  (2025-11-14T18:42:18Z)
• UserRL: Training Interactive User-Centric Agent via Reinforcement Learning [104.63494870852894]
  Reinforcement learning (RL) has shown promise in training agentic models that engage in dynamic, multi-turn interactions.<n>We propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments.
  arXiv  Detail & Related papers  (2025-09-24T03:33:20Z)
• Training a Generally Curious Agent [77.61142660542599]
  Paprika is a fine-tuning approach that enables language models to develop general decision-making capabilities.<n>Paprika teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates.<n>Results suggest a promising path towards AI systems that can autonomously solve sequential decision-making problems.
  arXiv  Detail & Related papers  (2025-02-24T18:56:58Z)
• Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents [44.34340798542]
  Large Language Models (LLMs) have shown remarkable capabilities in natural language tasks requiring complex reasoning. Traditional supervised pre-training on static datasets falls short in enabling autonomous agent capabilities. We propose a framework that combines guided Monte Carlo Tree Search (MCTS) search with a self-critique mechanism and iterative fine-tuning on agent interactions.
  arXiv  Detail & Related papers  (2024-08-13T20:52:13Z)
• Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection [1.4530711901349282]
  Test-Time Adaptation (TTA) has emerged as a promising strategy for tackling the problem of machine learning model robustness under distribution shifts. We evaluate existing TTA methods using surrogate-based hp-selection strategies to obtain a more realistic evaluation of their performance.
  arXiv  Detail & Related papers  (2024-07-19T11:58:30Z)
• Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
  Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
  arXiv  Detail & Related papers  (2024-07-02T10:09:19Z)
• Improving Generalization of Alignment with Human Preferences through Group Invariant Learning [56.19242260613749]
  Reinforcement Learning from Human Feedback (RLHF) enables the generation of responses more aligned with human preferences. Previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples. We propose a novel approach that can learn a consistent policy via RL across various data groups or domains.
  arXiv  Detail & Related papers  (2023-10-18T13:54:15Z)
• Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization [29.568222003322344]
  We introduce the Side-Information Assisted Regression (SIAR) framework, designed to identify game dynamics in multiplayer normal-form games. SIAR is solved using sum-of-squares (SOS) optimization, resulting in a hierarchy of approximations that provably converge to the true dynamics of the system. We showcase that the SIAR framework accurately predicts player behavior across a spectrum of normal-form games, widely-known families of game dynamics, and strong benchmarks, even if the unknown system is chaotic.
  arXiv  Detail & Related papers  (2023-07-13T09:14:48Z)
• Variational Information Pursuit for Interpretable Predictions [8.894670614193677]
  Variational Information Pursuit (V-IP) is a variational characterization of IP which bypasses the need for learning generative models. V-IP finds much shorter query chains when compared to reinforcement learning which is typically used in sequential-decision-making problems. We demonstrate the utility of V-IP on challenging tasks like medical diagnosis where the performance is far superior to the generative modelling approach.
  arXiv  Detail & Related papers  (2023-02-06T15:43:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.