Papers with exploration efficiency
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning (2026.acl-short)
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| Challenge: | Existing studies have proposed a new approach to optimize for SFT followed by RL . existing studies have suggested a method to optimize SFT for large language models . |
| Approach: | They propose a framework that encourages diversity based on token exploration space. |
| Outcome: | Experiments show that SED-SFT significantly improves generation diversity with a negligible computational overhead increase over CE loss. |
EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration (2026.findings-acl)
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| Challenge: | Existing geo-spatial question answering benchmarks focus on static retrieval, failing to capture the complexity of real-world planning that involves dynamic user locations and compound constraints. |
| Approach: | They propose a new benchmark for Large Language Models that integrates location-anchored and dual-objective queries with a user's real-time coordinates. |
| Outcome: | The proposed model can summarize historical exploration trajectories to enhance exploration efficiency. |
ProCeedRL: Process Critic with Explorative Demonstration Reinforcement Learning for LLM Agentic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit exceptional reasoning capabilities, driven by Reinforcement Learning with Verifiable Rewards (RLVR). |
| Approach: | They propose a method that uses a process-level critic to monitor interactions in real time, incorporating reflection-based demonstrations to guide agents in stopping the accumulation of errors. |
| Outcome: | The proposed approach exceeds the model’s saturated exploration performance and achieves superior performance on complex deep search and embodied tasks. |
DiaLLMs: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction (2025.findings-acl)
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| Challenge: | Existing medical LLMs focus primarily on diagnosis recommendation, limiting their clinical applicability. |
| Approach: | They propose a medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues. |
| Outcome: | The proposed model outperforms baselines in clinical test recommendation and diagnosis prediction. |
GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration (2025.emnlp-main)
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| Challenge: | Recent work of GUI action grounding fine-tunes data from pre-trained MLLMs, but data is limited to specific GUI environments. |
| Approach: | They propose to use a GUI-based agent to collect environment-specific data and fine-tune GUI grounding models with the collected data. |
| Outcome: | The proposed model can be extended to other GUI environments to improve performance. |