What Makes Interaction Trajectories Effective for Training Terminal Agents?
The paper investigates the effectiveness of interaction trajectories in training terminal agents. It reveals that higher performance in standalone agents does not necessarily correlate with better teaching outcomes. The study emphasizes the importance of environment-grounded supervision in enhancing the generalization capabilities of agents.
- ▪The research highlights a 'pedagogical paradox' where lower-scoring agents can provide better teaching than higher-scoring ones.
- ▪Environment-Grounded Supervision (EGS) is identified as a key factor that helps students internalize robust problem-solving routines.
- ▪The findings suggest that the future of agent post-training should focus on the systematic design of interaction structures rather than just outcome-matching.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03461 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
| 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 | U-N8fI5T1ODN |
| 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)
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| 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:2606.03461 (cs) [Submitted on 2 Jun 2026] Title:What Makes Interaction Trajectories Effective for Training Terminal Agents? Authors:Sidi Yang, Chaofan Tao, Jierun Chen, Tiezheng Yu, Ruoyu Wang, Yuxin Jiang, Yiming Du, Wendong Xu, Jing Xiong, Taiqiang Wu, Lifeng Shang, Xiaohui Li, Ngai Wong, Haoli Bai View a PDF of the paper titled What Makes Interaction Trajectories Effective for Training Terminal Agents?, by Sidi Yang and 13 other authors View PDF HTML (experimental) Abstract:Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficulty, harness design, and student capacity.
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