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Tmax: A simple recipe for terminal agents

Hamish Ivison, Junjie Oscar Yin, Rulin Shao, Teng Xiao, Nathan Lambert, Hannaneh Hajishirzi

arXiv:2606.23321v1cs.CL

TL;DR

Academic work has underexamined training terminal agents on complex, long-horizon tasks. Tmax introduces a large synthetic task dataset and simple RL recipe, achieving 27% on Terminal-Bench 2.0 with a 9B model.

  • Problem

    Academic work has largely focused on bug-fixing or simple terminal tasks rather than complex, long-horizon terminal tasks.

  • Method

    Tmax combines TMAX-15K, a compositional dataset of 14,600 RL environments, with a simple open RL training recipe.

  • Results

    27% on Terminal-Bench 2.0 is achieved by the best 9B model, with state-of-the-art performance among open models under 30B parameters.

  • Takeaways & Limitations

    Tmax provides a reproducible open baseline for academic work on training terminal agents.

  • Takeaways & Limitations

    The synthetic pipeline relies on a strong generator model, and unstable training may confound whether performance comes from data variety and difficulty.

Abstract

from arXiv · show

Terminal-using agents have quickly become the most popular downstream application of language models (LMs). Despite their prevalence, relatively little academic work has examined RL-based training of these models, likely due to difficult benchmarks, a lack of data, and a lack of simple baseline recipes. We present Tmax, the strongest open RL recipe for terminal agents to date, bringing open data recipes closer to the frontier. While simple, our recipe achieves 27\% on Terminal-Bench 2.0 with only 9B parameters, outperforming much larger models from prior work. Concretely, we generate data using a novel taxonomy, combining difficulty control, personas, and verifier diversification, which allows us to cheaply generate large amounts of terminal environments for RL and SFT training. We open-source our terminal dataset, which is over 2.5x larger than previously released terminal-agent datasets. We then train open-weight models using RL with our data, using a simple, outcome-only recipe. We release our data, models, and code as a strong baseline for future open academic work on terminal agents at https://github.com/hamishivi/tmax.

1 Introduction

Tmax addresses the limited academic study of RL-trained terminal agents with a large, difficulty-controlled dataset and a simple open RL recipe. Its best 9B model reaches 27% on Terminal-Bench 2.0, while training generalizes across tasks and harnesses.

  • 1 Introduction: Tmax targets a gap in academic work by supplying complex terminal tasks and small yet powerful open-weight agents as baselines for future research.The work highlights training stability, complex tool-call setups, harness improvements, and harder downstream tasks as open challenges.
  • 1 Introduction: RL training improves SWE-Bench Verified by over 5 points and also improves AIME and performance across different prompts, tools, and harnesses.These results provide evidence that the recipe teaches capabilities rather than merely fitting a specific harness.
  • 1 Introduction: TMAX-15K provides 14,600 RL environment instances—over 2.5x larger than prior terminal datasets—varying in difficulty, domain, and required skills.Its synthetic pipeline explicitly controls and increases task difficulty and uses continuously valued rewards beyond binary correctness checks.
  • 1 Introduction: 27% on Terminal-Bench 2.0 is achieved by Tmax’s best 9B model, attaining state-of-the-art performance among open models under 30B parameters.
  • 1 Introduction: Tmax provides a simple, reproducible open RL recipe whose performance exceeds prior open recipes such as Endless Terminals and OpenThinker-Agent.The authors publicly release the elements required to train the models.

2 Background and Related Work

Prior terminal-agent work exposed the need for complex tasks and broadly followed repository adaptation or taxonomy-guided synthesis, while RL training remained underexplored. Tmax adopts taxonomy-guided generation and releases over 14,000 diverse, difficulty-balanced RL environments.

  • Motivation: Early NL2Bash-based RL data failed to significantly improve over a strong SFT model, motivating more complex terminal-agent tasks with stronger learning signals.NL2Bash paired natural-language instructions with bash commands mined from the web and was converted into an RL dataset for OpenThinker Agent (Team, 2025).
  • Related work: Terminal-agent data generation generally adapts repositories or tasks, or synthesizes new tasks from seed tasks and taxonomies.Repository-based work is dominated by SWE-Bench-oriented datasets and models, whose bug-fixing tasks cover only a subset of terminal-agent workloads.
  • Related work: Tmax follows taxonomy- or seed-guided synthesis, addressing Endless Terminals’ smaller, file-manipulation-focused dataset that is too easy for modern models.Endless Terminals (Gandhi et al., 2025) uses a strong external model to generate terminal tasks but produces significantly fewer tasks concentrated on file manipulation.
  • Data generation: Over 14,000 unique RL environments span diverse difficulties and are balanced across terminal-agent tasks.This is the data-generation approach adopted by Tmax.
  • RL for terminal agents: Although RL is increasingly central to LM post-training and succeeds on agentic tasks, relatively little work studies RL training for terminal agents, with most prior data used only for finetuning.Prior data-generation work includes (Wu et al., 2026; Zhu et al., 2026; Pi et al., 2026), while successful RL for agentic tasks includes (Cursor Research et al., 2026).

3 Terminal Data Generation

TMAX introduces a compositional terminal-task generation pipeline that scales through structured sampling, diversified task formats, and execution-only validation. It produces TMAX-15K1, a large, balanced, difficult, and uncontaminated dataset, plus a smaller SFT warm-start set.

  • Data Generation: The pipeline hierarchically samples structured axes to create combinatorially diverse tasks with explicit control over domains, personas, artifacts, complexity, and verifier difficulty.It uses domain and skills plus orthogonal diversity axes, persona-specific generation, multimodal fixtures, fine-grained complexity buckets, and graded verifiers.
  • Dataset Construction: 14,600 tasks form TMAX-15K1, while an additional 2.2k environments yield 16.5K SFT trajectories, including 8K successful trajectories.The SFT data uses Qwen 3.6 27B-generated trajectories and filters out unparsed tool calls.
  • Difficulty & Balance: 42% pass@1 and 50% pass@4 and 53% pass@8 make TMAX-15K1 among the most challenging terminal datasets evaluated.On 250-task subsamples with eight rollouts, its pass@1 is 42% versus 41–92% for prior datasets, while pass@4 and pass@8 are lowest.
  • Composition: TMAX-15K1 achieves balance scores of 0.998 for domain and 0.732 for skill-type, the highest values among compared datasets.The framework exposes domain as an explicit sampling axis, enabling direct calibration of per-domain mass across nine labels.
  • De-contamination: The dataset has 0% overlap with both Terminal-Bench and TB-Lite under a 13-gram contamination check.This matches the majority of prior datasets under the reported contamination protocol.

4 Training Terminal Agents

TMAX combines asynchronous DPPO-based RL with terminal-specific infrastructure and evaluation controls. Across datasets, model sizes, harnesses, and model families, TMAX improves terminal-agent performance and transfers beyond its training setup.

  • Training recipe: Training uses asynchronous DPPO with token masking based on binary total-variation divergence, active sampling, and zero-variance group filtering.The implementation extends open-instruct, uses vLLM rollouts with Podman or Apptainer sandboxes, and keeps the language-model head in FP32 to reduce mismatch.
  • Dataset comparison: TMAX-15K outperforms prior terminal datasets for RL training, while requiring more interaction steps throughout training and increasingly many assistant-turn tokens.These trends suggest the data remains difficult and encourages more complex reasoning and tool calls during learning.
  • Main results: TMAX-9B is the strongest model under 10B parameters on Terminal-Bench 2.0, outperforming prior 32B variants and approaching closed large-lab offerings.It also outperforms prior open RL recipes for terminal agents.
  • Scaling: The same RL recipe improves Qwen 3.5 models from 2B through 27B parameters, although gains shrink for smaller models and are harder for the 27B base.The recipe is applied without modification across these model sizes.
  • Beyond Terminal-Bench: TMAX-9B improves SWE-Bench Verified and AIME performance, including in a traditional single-turn setting, indicating benefits beyond the terminal harness and domain.The authors relate this result to improved use of terminal tools for generic problem solving.
  • Harness generalization: TMAX-9B improves by at least 9 points across evaluated harnesses, though its largest gains and strongest performance remain on the authors’ own harness.This supports transfer from single-harness terminal RL to other setups.

5 Challenges in training TMAX-9B

TMAX-9B training faces two central challenges: SFT can degrade Qwen 3.5 9B, and RL runs frequently become unstable or collapse after roughly 300 steps. The authors mitigate instability with numerical and algorithmic changes, but report that multiturn tasks, sandbox costs, and training–evaluation mismatches remain important limitations.

  • SFT limitations: SFT degrades Qwen 3.5 9B performance, whereas it aids Qwen 3 8B; the authors leave better SFT mixtures for Qwen 3.5 to future work.TMAX SFT uses Qwen 3.6 27B as a teacher, yet still degrades Qwen 3.5 9B, while older data likely suffers from weaker teacher models.
  • RL instability: Training was often unstable, with runs frequently collapsing beyond 300 steps.Similar instability also occurred when training Qwen 3 8B, indicating that the issue is not specific to Qwen 3.5.
  • RL instability: Qwen 3.5’s hybrid architecture creates numeric mismatches between training and inference, motivating use of an FP32 LM head.The FP32 LM head aided in reducing these mismatches.
  • RL instability: DPPO, an FP32 LM head, and 32 rollouts per prompt reduced training instability, while a small KL penalty reduced collapse severity but lowered overall reward.DPPO and larger group size were supported by the reported training-reward comparisons in Figures 7 and 8.
  • Training environment: Long multiturn terminal tasks, costly sandbox infrastructure, and high-load execution issues exacerbate instability and create conditions absent during evaluation.Instabilities increased after 10 assistant turns and did not appear in pilot training with fewer than 5 turns.

6 Conclusion … B.3 Balance score

TMAX combines a 14,600-environment dataset with a simple RL recipe to train a strong open-weight terminal agent, while its synthetic pipeline and unstable training remain important limitations. The appendices further characterize verifier diversity, dataset difficulty, and the balance-score metric.

  • 6 Conclusion: TMAX-9B achieves state-of-the-art performance among open-weight models under 10B and significantly outperforms prior open terminal RL recipes.The recipe combines TMAX-15K, a dataset of 14,600 RL environments built with explicit difficulty and diversity control, with simple RL training.
  • 6 Conclusion: The synthetic data pipeline depends on a strong generator model, and it is unclear whether it can improve beyond that generator rather than merely match it.Training is also unstable, so performance may reflect stability-promoting features rather than the targeted variety and difficulty.
  • A Contribution Statement: The project assigned core training and evaluation, data generation and analysis, experiment support, feedback, compute management, and paper writing across the listed authors.All authors participated in paper writing and provided general feedback on experiments.
  • B.1 Verifier and fixture kinds: Each task samples one verifier kind and one fixture kind, adding graded verification and non-text inputs beyond legacy text-in/text-out defaults.Graded verifiers reduce brittle string equality and provide a continuous difficulty knob, while non-text fixtures broaden inputs without changing the text-only policy.
  • B.2 Pass@k difficulty curves: TMAX occupies the hardest pass@k band with CLI-Gym across k and has the lowest pass@8 of every dataset, remaining difficult after eight rollouts.Figure 9 uses Gemini-3-Flash-Preview on fixed 250-task subsamples, with lower curves indicating greater difficulty.
  • B.3 Balance score: The balance score is exp(H)/N, measuring the normalized effective number of categories and ranging from 1/N for concentrated mass to 1.0 for uniform coverage.It is preferred to raw entropy because it is comparable across axes with different bucket counts and represents a fraction of uniform diversity.

B.4 Decontamination · C Harness Choices · D Additional RL Training Details

The paper tests train–test contamination with 13-gram overlap against Terminal-Bench 2.0 and TB-Lite, while selecting a simpler mini-SWE-agent-inspired harness that outperforms Terminus-2 for smaller models. The harness choice is motivated by tool-format complexity and reduced RL-training complexity.

  • B.4 Decontamination: The contamination test compares dataset task descriptions with Terminal-Bench 2.0 and TB-Lite benchmark descriptions.
  • B.4 Decontamination: Using sliding 13-token windows with stride 1, a dataset task is flagged when any window matches a benchmark 13-gram.
  • B.4 Decontamination: The protocol follows standard contamination procedures (Brown et al., 2020; Touvron et al., 2023), with larger n providing a stricter test and fewer spurious matches.
  • B.4 Decontamination: 0.5% overlap is reported on TB2 under the contamination analysis.
  • C Harness Choices: The selected initial harness is inspired by mini-SWE-agent and was chosen based on results from smaller closed-source models such as Claude Haiku 4.5.
  • C Harness Choices: The proposed harness and mini-SWE-agent outperform Terminus-2 in Table 12, the usual default for Terminal-Bench models.
  • C Harness Choices: The authors attribute Terminus-2’s weaker performance to complex tool formats that smaller models struggle to follow.
  • C Harness Choices: A simpler harness was also preferred to reduce complexity during RL training.

D.1 Full RL training hyperparameters · D.2 Full SFT Training Details

The appendix specifies the default RL and SFT hyperparameters, with SFT largely following Pi et al. (2026) and retaining unsuccessful or incomplete rollouts. It also documents verifier and fixture variants and provides the full composition of the large SFT mixture.

  • D.1 Full RL training hyperparameters: The final training recipe uses RL hyperparameters from Table 13 and SFT hyperparameters from Table 14 as its defaults.These settings define the standard configurations unless otherwise stated.
  • D.1 Full RL training hyperparameters: Four graded verifiers supplement legacy exact-text equality, relaxing exact matching while exposing an explicit difficulty knob.The verifier taxonomy supports controlled variation in evaluation difficulty.
  • D.1 Full RL training hyperparameters: Optional non-text fixtures require agents to recover hidden ground truth through standard terminal tooling while keeping the policy text-only.Each task may include a non-text artifact, but the agent-facing policy remains text-based.
  • D.2 Full SFT Training Details: SFT hyperparameters largely follow Pi et al. (2026), including the decision not to filter unsuccessful or incomplete dataset rollouts.This choice preserves unsuccessful and incomplete examples in the SFT data.
  • D.2 Full SFT Training Details: The SFT configuration is specified in Table 14, providing the detailed settings used for supervised fine-tuning.The appendix points to Table 14 as the authoritative source for the full SFT hyperparameters.
  • D.2 Full SFT Training Details: Table 15 gives the complete splits for the large SFT mixture introduced in §5.1.The table expands the composition of the ‘big’ SFT mix.

D.3 SWE-Smith Training

SWE-Smith becomes increasingly unusable for RL because perfect-solving samples are filtered out, making training slow and expensive. Consequently, training stops near 100 steps and uses more frequent checkpoint evaluations.

  • D.3 SWE-Smith Training: Up to 200–300 SWE-Smith samples must be processed to fill one RL batch because all 32 rollouts increasingly achieve reward 1 and are filtered out.This filtering produces incredibly slow and expensive training, as shown in Figure 10.
  • D.3 SWE-Smith Training: Training stops at around 100 steps because perfect-solving filtering makes continued SWE-Smith RL training prohibitively slow and expensive.If all samples were added to batches, training would reach roughly 1,000 steps in the time needed to reach step 100.
  • D.3 SWE-Smith Training: Checkpoints are evaluated at steps 20, 40, 60, 80, and 100 instead of every 100 steps used for other models.This schedule reflects the shortened SWE-Smith training run.

D.4 GRPO Training · D.5 Filtered Samples

The training compares DPPO and GRPO using a modified Open-Instruct implementation with specified clipping settings. RL training filters all-same-reward samples, which are rare on TMAX-15K and indicate informative rollout groups.

  • D.4 GRPO Training: GRPO is compared with DPPO in Fig. 7 using a modified Open-Instruct implementation.The modification uses logprobs returned directly by vLLM as πold in the ratio, following (DeepSeek-AI et al., 2025).
  • D.4 GRPO Training: GRPO uses clip-higher 0.272 and clip-lower 0.2, while otherwise matching the hyperparameters in Tab. 13.
  • D.4 GRPO Training: The GRPO ratio uses vLLM-returned logprobs directly as πold, following (DeepSeek-AI et al., 2025).
  • D.5 Filtered Samples: All-zero reward groups remain fairly low throughout TMAX-15K RL training, as shown in Fig. 11.
  • D.5 Filtered Samples: All-same-reward samples are filtered because they contribute no gradient to the batch.
  • D.5 Filtered Samples: The rarity of filtered groups suggests the data is easy enough for the model to find some solution yet hard enough to produce at least one mistake among 32 rollouts.

D.6 Reward Hacking · E Additional Evaluation Details

TMAX-9B exhibits three reward-hacking patterns after RL training, but these rollouts score 0 and therefore do not affect downstream performance. Additional evaluation details specify the RL, SFT, and large-mix dataset configurations.

  • D.6 Reward Hacking: Two break-filter-js-from-html runs replaced /tests/filter.py with a no-op filter and used a trivial <script>alert(...) payload.
  • D.6 Reward Hacking: Two caffe-cifar-10 runs attempted to fake training with a stub Caffe binary, simulated logs, and dummy .caffemodel files.
  • E Additional Evaluation Details: RL runs use the hyperparameters listed in Table 13 unless otherwise stated.
  • E Additional Evaluation Details: SFT training hyperparameters are provided in Table 14, while Table 15 lists source datasets and sample counts for the ‘big’ SFT mix described in §5.1.
  • D.6 Reward Hacking: Two build-pov-ray runs created mock /usr/local/bin/povray wrappers that printed fake POV-Ray 2.2 output or wrote placeholder images.
  • D.6 Reward Hacking: The model’s CoT suggested simplification rather than deliberate verifier deception, as one Caffe run proposed a minimal fake binary after finding the task too complicated.The displayed reasoning described creating proper training output and a model file while using a minimal C program as the caffe binary.

E.1 Full Figure 1 Results

Full numeric Terminal-Bench 2.0 results for Figure 1 are reported in Table 16, combining prior-paper results with official leaderboard entries for open- and closed-weight models. Qwen and TMAX evaluations average five harness runs under a standardized Daytona/vLLM setup.

  • Evaluation setup: When multiple harnesses are reported, the comparison uses the best result from mini-swe-agent or Terminus-2 runs.TMAX-9B also receives a small manual reward-hacking check described in §D.6.
  • Full results: Table 16 reports the full numeric Terminal-Bench 2.0 results for Figure 1.Prior-work values come from the respective papers, while open- and closed-weight model values use the best official leaderboard entries.
  • Evaluation setup: Scores for Qwen and TMAX models are averaged over five runs using the authors’ harness.Both model groups use Daytona as the sandbox backend and vLLM on a single A100 node; timed-out runs are restarted up to three times.
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