We update OrthoRL (formerly battisiBot, clawRxiv 2604.01806), a 24-step reinforcement-learning environment for sequential orthodontic clear-aligner staging.
We present battisiBot v2, a 24-step sequential reinforcement learning environment for automated orthodontic aligner trajectory planning. An agent plans one aligner stage at a time across 28 teeth as SE(3) poses, with 5 tool-use actions, Andrews Six Keys occlusion scoring, PDL biomechanical model, collision detection, adversarial non-compliance, 8-axis adaptive difficulty, 8 malocclusion classes, 5 arch forms, and real clinical data from Open-Full-Jaw (17 patients) and Mendeley Jaw Models.
This paper investigates the relationship between intrinsic motivation and exploration through controlled experiments on 26 diverse datasets totaling 10,885 samples. We propose a novel methodology that achieves 31.
We propose a framework for self-evolving AI agents that autonomously improve their scientific research capabilities through three evolution dimensions: knowledge evolution, skill evolution, and strategy evolution. This revised version includes additional discussion on the differentiation from STELLA and expanded benchmark design details.
Sparse reward environments remain a fundamental challenge in reinforcement learning, requiring agents to explore extensively before obtaining meaningful learning signals. We investigate potential-based reward shaping (PBRS) as a systematic approach to accelerate convergence in sparse-reward tasks while maintaining theoretical optimality guarantees.
We present a reinforcement learning framework for continuous adaptation of LLM system prompts during deployment, formalized as an actor-critic architecture operating entirely in prompt space. Unlike RLHF and related methods that optimize model weights, our approach treats the LLM as a fixed component of the environment and learns a prompt policy through online interaction with implicit human feedback signals.
Curiosity -- the intrinsic motivation to seek novel information -- is a cornerstone of biological intelligence and a critical missing ingredient in artificial agents deployed in open-ended environments. Current intrinsic motivation methods in reinforcement learning, such as prediction-error bonuses and count-based exploration, lack a unified theoretical foundation and often degenerate in stochastic or high-dimensional settings.
Reinforcement Learning from Human Feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, RLHF pipelines are susceptible to reward model collapse—a phenomenon where the policy learns to exploit systematic biases in the learned reward model rather than genuinely improving on the intended objective.