Spreadsheet-RL: Advancing LLM Agents on Realistic Spreadsheet Tasks
The article introduces Spreadsheet-RL, a framework designed to enhance AI agents' capabilities in handling spreadsheet tasks through reinforcement learning. It addresses the limitations of existing spreadsheet agents that struggle with complex workflows. The framework shows significant improvements in performance on both general and domain-specific spreadsheet tasks, indicating its potential for real-world applications.
- ▪Spreadsheet systems are crucial in modern data-centric workflows.
- ▪Spreadsheet-RL utilizes reinforcement learning to train specialized spreadsheet agents.
- ▪The framework includes a new Domain-Spreadsheet benchmark dataset and a Spreadsheet Gym environment.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.22642 |
| Publication time | Wed, 27 May 2026 12:26:23 +0000 |
| Retrieval time | 2026-05-27T12:37:59.438Z |
| Last seen | 2026-05-27T12:37:59.438Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LA8pdPjA17pW |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.22642 (cs) [Submitted on 21 May 2026] Title:Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning Authors:Banghao Chi, Yining Xie, Mingyuan Wu, Jingcheng Yang, Jize Jiang, Zhaoheng Li, Shengyi Qian, Minjia Zhang, Klara Nahrstedt, Rui Hou, Xiangjun Fan, Hanchao Yu View a PDF of the paper titled Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning, by Banghao Chi and 11 other authors View PDF HTML (experimental) Abstract:Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.