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The paper proposes CITA, a method that trains a Comparative Inference Model (CIM) to estimate the long-horizon value of possible next tool invocations before executing them, improving Tool F1 and task success across three tool-use benchmarks and multiple LLM backbones.
The paper proposes RSIQL, a non-hierarchical method for offline goal-conditioned reinforcement learning that uses selective reward stimulation to improve performance on benchmarks like D4RL.
This paper proposes CSDG, a method for offline reinforcement learning that expresses Bellman backups as in-sample targets plus a convex-hull-neighborhood local correction, controlling OOD action estimation errors and improving value stability.
The paper presents World Value Model (WVM), a generalist robotic value model that combines world models with value estimation to accurately assess task progression and improve robotic policy learning from mixed-quality data, achieving state-of-the-art results on standard benchmarks and a new suboptimal data benchmark.
This paper introduces POISE, a method for stable policy optimization in large reasoning models by estimating baselines using the model's own internal states, reducing computational overhead compared to PPO and GRPO.