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arxiv: 2606.03461 · v1 · pith:2UU4IA66new · submitted 2026-06-02 · 💻 cs.AI

What Makes Interaction Trajectories Effective for Training Terminal Agents?

Sidi Yang , Chaofan Tao , Jierun Chen , Tiezheng Yu , Ruoyu Wang , Yuxin Jiang , Yiming Du , Wendong Xu
show 6 more authors
Jing Xiong Taiqiang Wu Lifeng Shang Xiaohui Li Ngai Wong Haoli Bai
This is my paper

Pith reviewed 2026-06-28 10:15 UTC · model grok-4.3

classification 💻 cs.AI
keywords terminal agentsinteraction trajectoriesenvironment-grounded supervisionagent post-trainingpedagogical paradoxharness engineeringTerminal-LegoTerminal-Bench
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The pith

Trajectories from a lower-scoring agent produce stronger generalization in fine-tuned terminal agents than those from a higher-scoring agent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests the common assumption that stronger standalone agents make better teachers for post-training. It finds the opposite: students fine-tuned on trajectories from DeepSeek-V3.2 outperform those trained on Claude Opus 4.6 trajectories despite the latter's higher benchmark scores. The difference is traced to Environment-Grounded Supervision, in which trajectories visibly display inspect-act-verify cycles that let students acquire robust routines instead of brittle action sequences. A scalable pipeline called Terminal-Lego converts real-world issues into verifiable tasks, and scaling experiments show that 15.3k such trajectories suffice for competitive performance previously requiring over 30 times more data. The work therefore reframes agent post-training around deliberate harness design rather than outcome matching alone.

Core claim

Standalone performance does not dictate teaching efficacy. Students fine-tuned on trajectories from DeepSeek-V3.2, a lower-scoring agent, exhibit significantly stronger generalization than those trained on trajectories from Claude Opus 4.6. This pedagogical paradox arises because trajectories that explicitly expose inspect-act-verify behaviors through harness-visible interactions allow students to internalize robust problem-solving routines rather than fragile action sequences.

What carries the argument

Environment-Grounded Supervision (EGS): trajectories that explicitly expose inspect-act-verify behaviors through harness-visible interactions.

If this is right

  • Agent post-training should prioritize trajectories that make inspect-act-verify loops visible over trajectories from the strongest possible teacher agent.
  • Harness engineering that systematically exposes environment interactions can reduce the data volume needed for high performance.
  • Reproducible gains in terminal-agent capability become possible by focusing on interaction structure rather than raw outcome matching.
  • Scaling laws for agent training shift from total data volume toward the density of environment-grounded signals per trajectory.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same EGS principle could be tested in non-terminal agent domains such as web navigation or tool-use chains where harness visibility can be engineered.
  • Future work might quantify EGS density as a measurable property of any trajectory dataset and use it to rank candidate teachers without running full student fine-tunes.
  • If harness design is the primary lever, then open-source harnesses could become the main shared resource for agent research instead of proprietary model weights.

Load-bearing premise

Differences in teaching efficacy are caused by the presence of Environment-Grounded Supervision rather than uncontrolled factors such as task difficulty distribution, harness design details, or student model capacity.

What would settle it

An experiment that edits the same set of trajectories to either hide or preserve the explicit verify steps and then measures whether the generalization gap between the two teacher agents disappears.

Figures

Figures reproduced from arXiv: 2606.03461 by Chaofan Tao, Haoli Bai, Jierun Chen, Jing Xiong, Lifeng Shang, Ngai Wong, Ruoyu Wang, Sidi Yang, Taiqiang Wu, Tiezheng Yu, Wendong Xu, Xiaohui Li, Yiming Du, Yuxin Jiang.

Figure 1
Figure 1. Figure 1: The Pedagogical Paradox: Discrepancy between standalone performance and teaching efficacy. While Claude Opus 4.6 achieves the highest standalone score on Terminal-Bench 2.0, its tra￾jectories produce significantly weaker students compared to those from DeepSeek-V3.2. We attribute this gap to the alignment between actions and environmental feedback: teachers that prioritize actions rigorously supported by p… view at source ↗
Figure 2
Figure 2. Figure 2: Terminal-Lego construction pipeline. StackOverflow issues are filtered into realistic sources, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of Stack Overflow Source Questions Across Domains [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training loss curves when fine-tuning Qwen3-32B on trajectories from different teachers. [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Gradient norm curves during fine-tuning. Claude Opus 4.6 exhibits the highest gradient [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
read the original 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. We investigate this pedagogical link using Terminal-Lego, a scalable pipeline that transforms multi-domain real-world issues into environment-verified agentic tasks. Surprisingly, standalone performance does not dictate teaching efficacy: while Claude Opus 4.6 achieves higher scores on Terminal-Bench 2.0, students fine-tuned on trajectories from DeepSeek-V3.2, a lower-scoring agent, exhibit significantly stronger generalization. We attribute this "pedagogical paradox" to Environment-Grounded Supervision (EGS): trajectories that explicitly expose inspect-act-verify behaviors through harness-visible interactions allow students to internalize robust problem-solving routines rather than fragile action sequences. Scaling analysis reveals exceptional data efficiency: with only 15.3k Terminal-Lego trajectories, for example, Qwen3-32B achieves a 24.3% score on Terminal-Bench 2.0, rivaling previous SOTA performance established with over 30x the data volume. Our results suggest that the frontier of agent post-training lies beyond mere outcome-matching, shifting the focus toward "Harness Engineering", where the systematic design of environment-grounded interaction structures serves as the primary catalyst for reproducible and generalizable agentic intelligence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that stronger standalone performance does not make an agent a better teacher for post-training terminal agents. Using the Terminal-Lego pipeline to generate environment-verified tasks, it shows that fine-tuning on trajectories from DeepSeek-V3.2 (lower Terminal-Bench 2.0 score) produces students with significantly stronger generalization than those trained on Claude Opus 4.6 trajectories. The authors attribute this to Environment-Grounded Supervision (EGS) in the form of harness-visible inspect-act-verify loops and report that 15.3k such trajectories suffice for Qwen3-32B to reach 24.3% on Terminal-Bench 2.0, rivaling prior SOTA with >30x more data.

Significance. If the causal role of EGS can be isolated, the result would usefully redirect agent post-training research from pure outcome matching toward systematic harness design that elicits grounded interaction patterns, with the reported data-efficiency numbers providing a concrete empirical anchor for that shift.

major comments (2)
  1. [Abstract] Abstract: the central claim that EGS (rather than uncontrolled differences in task difficulty distribution, harness design, error patterns, or trajectory statistics) explains the generalization gap is not supported by any described controls or ablations that hold those factors fixed while varying only the presence of harness-visible inspect-act-verify loops.
  2. [Experiments] The comparison between DeepSeek-V3.2 and Claude Opus 4.6 trajectories simultaneously varies capability, interaction style, and error distribution; without an experiment that decouples EGS from these covariates (e.g., via synthetic trajectory editing or matched-difficulty subsets), the pedagogical-paradox attribution remains correlational.
minor comments (1)
  1. The scaling result (15.3k trajectories, 24.3% score) is presented without the exact baseline data volume, model sizes, or statistical error bars that would allow direct comparison to the cited prior SOTA.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the need to better isolate the causal contribution of Environment-Grounded Supervision. We agree that the current evidence for attributing the generalization gap specifically to harness-visible inspect-act-verify loops remains correlational, as multiple factors differ between the DeepSeek-V3.2 and Claude Opus 4.6 trajectory sets. In the revised manuscript we will explicitly acknowledge this limitation, temper the strength of the EGS claim in the abstract and discussion, and add analyses of matched-difficulty subsets where possible.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that EGS (rather than uncontrolled differences in task difficulty distribution, harness design, error patterns, or trajectory statistics) explains the generalization gap is not supported by any described controls or ablations that hold those factors fixed while varying only the presence of harness-visible inspect-act-verify loops.

    Authors: We accept this assessment. The manuscript currently infers the role of EGS from observed trajectory differences and downstream performance but does not present ablations that isolate harness-visible loops while holding task difficulty, error patterns, and other statistics fixed. We will revise the abstract to present the EGS interpretation as a hypothesis supported by correlational evidence rather than a demonstrated causal mechanism, and we will add a limitations paragraph outlining the required controls. revision: yes

  2. Referee: [Experiments] The comparison between DeepSeek-V3.2 and Claude Opus 4.6 trajectories simultaneously varies capability, interaction style, and error distribution; without an experiment that decouples EGS from these covariates (e.g., via synthetic trajectory editing or matched-difficulty subsets), the pedagogical-paradox attribution remains correlational.

    Authors: The referee is correct that the agent comparison confounds multiple variables. Our trajectory analysis shows systematic differences in inspect-act-verify patterns, but we lack experiments that hold other covariates constant. We will incorporate a matched-difficulty subset analysis in the revision to partially address this. Full synthetic trajectory editing to isolate EGS is methodologically complex and may introduce new artifacts; we therefore treat it as future work rather than a revision deliverable. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical comparison with observational attribution

full rationale

The paper is an empirical study that generates trajectories from two teacher agents, fine-tunes student models on them, and reports generalization differences on Terminal-Bench 2.0. It then attributes the observed advantage to Environment-Grounded Supervision (EGS) on the basis of qualitative differences in the trajectories. No equations, fitted parameters renamed as predictions, ansatzes, or uniqueness theorems appear in the provided text. The central claim is an observational inference rather than a derivation that reduces to its own inputs by construction. Self-citations, if present, are not load-bearing for any mathematical step. The noted confounders (task difficulty, harness details, student capacity) affect causal interpretation but do not constitute circularity under the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond the introduced term EGS; full paper would be needed to audit modeling choices.

invented entities (1)
  • Environment-Grounded Supervision (EGS) no independent evidence
    purpose: Explains why certain trajectories enable better generalization by exposing inspect-act-verify loops
    Term introduced in abstract to account for the observed pedagogical paradox.

pith-pipeline@v0.9.1-grok · 5815 in / 1034 out tokens · 22822 ms · 2026-06-28T10:15:52.031226+00:00 · methodology

discussion (0)

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