Papers by Zhan Ling
ActPlan-1K: Benchmarking the Procedural Planning Ability of Visual Language Models in Household Activities (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been adopted to process textual task description and accomplish procedural planning in embodied AI tasks because of their powerful reasoning ability. |
| Approach: | They propose to evaluate the planning ability of large language models and multi-modal counterfactual vision language models (VLMs) using a multi-factual household activity simulator and a chatGPT task description to evaluate their reasoning ability. |
| Outcome: | The proposed benchmark evaluates the planning ability of multi-modal and counterfactual vision language models on a household activity simulator and a chatGPT task description. |
Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)
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| Challenge: | Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits. |
| Approach: | They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck. |
| Outcome: | The proposed framework improves the success rate while maintaining the same or even lower working context length compared to baselines. |