Human Feedback Types
strongRubric Rating
Directly usable for protocol triage.
"Small LLMs often struggle to match the agentic capabilities of large, costly models."
HFEPX · Eval paper review
Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards
Yuanjie Lyu, Chengyu Wang, Lei Shen, Jun Huang +1 more
Published
Jan 30, 2026
Citations
0
Trust level
Moderate
Usefulness score
57/100 (Medium)
Extraction confidence
65% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Mar 12, 2026
Should you rely on this paper?
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The abstract does not clearly name benchmarks or metrics.
Best use
Secondary protocol comparison source
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly name benchmarks or metrics.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
57/100
Moderate-confidence candidate
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Abstract
Small LLMs often struggle to match the agentic capabilities of large, costly models. While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes. We address these challenges with SYNTHAGENT, a framework that jointly synthesizes diverse tool-use training data and simulates complete environments. Specifically, a strong teacher model creates novel tasks and tool ecosystems, then rewrites them into intentionally underspecified instructions. This compels agents to actively query users for missing details. When handling synthetic tasks, an LLM-based user simulator provides user-private information, while a mock tool system delivers stable tool responses. For rewards, task-level rubrics are constructed based on required subgoals, user-agent interactions, and forbidden behaviors. Across 14 challenging datasets in math, search, and tool use, models trained on our synthetic data achieve substantial gains, with small models outperforming larger baselines.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
strongSimulation Env
Includes extracted eval setup.
"Small LLMs often struggle to match the agentic capabilities of large, costly models."
Quality Controls
missingNot reported
No explicit QC controls found.
"Small LLMs often struggle to match the agentic capabilities of large, costly models."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Small LLMs often struggle to match the agentic capabilities of large, costly models."
Reported Metrics
missingNot extracted
No metric anchors detected.
"Small LLMs often struggle to match the agentic capabilities of large, costly models."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Rubric Rating
- Rater population
- Not reported
- Unit of annotation
- Multi Dim Rubric
- Expertise required
- Math
Evaluation details
- Evaluation modes
- Simulation Env
- Agentic eval
- Tool Use
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Secondary protocol comparison source
Research brief
Metadata summary
Small LLMs often struggle to match the agentic capabilities of large, costly models.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Small LLMs often struggle to match the agentic capabilities of large, costly models.
- While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes.
- We address these challenges with SYNTHAGENT, a framework that jointly synthesizes diverse tool-use training data and simulates complete environments.
Researcher actions
- Compare this paper against others mentioning MATH.
- Validate inferred eval signals (Tool-use evaluation) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- Small LLMs often struggle to match the agentic capabilities of large, costly models.
- While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for…
- We address these challenges with SYNTHAGENT, a framework that jointly synthesizes diverse tool-use training data and simulates complete environments.
Why it matters for eval
- Small LLMs often struggle to match the agentic capabilities of large, costly models.
- While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for…
Researcher checklist
-
Human feedback protocol is explicit
Detected: Rubric Rating
-
Evaluation mode is explicit
Detected: Simulation Env
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.