Yichuan Ma
Publications
Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic
As large language models (LLMs) increasingly tackle complex reasoning tasks, test-time scaling has become critical for enhancing capabilities. However, in agentic scenarios with frequent tool calls, the traditional generation-length-based definition breaks down: tool latency decouples inference time from generation length. We propose Timely Machine, redefining test-time as wall-clock time, where models dynamically adjust strategies based on time budgets. We introduce Timely-Eval, a benchmark spanning high-frequency tool calls, low-frequency tool calls, and time-constrained reasoning. By varying tool latency, we find smaller models excel with fast feedback through more interactions, while larger models dominate high-latency settings via superior interaction quality. Moreover, existing models fail to adapt reasoning to time budgets. We propose Timely-RL to address this gap. After cold-start supervised fine-tuning, we use reinforcement learning to enhance temporal planning. Timely-RL improves time budget awareness and consistently boosts performance across Timely-Eval. We hope our work offers a new perspective on test-time scaling for the agentic era.
InternAgentHarness: A Scalable Synthetic Environment for Enhancing LLM Agentic Abilities
Large language models (LLMs) are increasingly expected to act as generalist agents capable of solving complex real-world problems. Training such agents, however, requires stable and diverse environments that support repeated interaction with stateful, tool-augmented tasks and provide verifiable feedback. Despite recent progress, the development of robust LLM agents remains limited by the lack of realistic, scalable, and executable training environments. We present InternAgentHarness, a scalable synthetic environment for improving the agentic capabilities of LLMs. Built upon the InternBootcamp training framework~\citep{internbootcampv1}, InternAgentHarness instantiates executable agent environments through a four-layer interface that unifies prompt generation, tool execution, interaction control, and reward computation. We further introduce \bootcampcli, an agent harness that automatically converts diverse agentic tasks into a Bootcamp-trainable paradigm. Unlike static benchmarks, InternAgentHarnessmakes evaluation actionable: observed failures can be systematically converted into new synthetic tasks, filtered trajectories, reinforcement learning rollouts, and subsequent re-evaluation under the same executable interface. We instantiate InternAgentHarness on a suite of 10 tasks covering both text-only and vision-based agent scenarios. Starting from Qwen3-VL-30B-A3B-Thinking, both supervised fine-tuning (SFT) and reinforcement learning (RL) on InternAgentHarness substantially improve performance over untuned model. These results suggest that InternAgentHarness provides a practical foundation for scalable synthetic agent environments and enables the continuous improvement of LLM agents through an iterative cycle of evaluation, synthesis, training, and refinement.