Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers
cs.LG, cs.AI, cs.CL
Submitted: 2026-09-21
Updated: 2026-09-21
Comments: 12 pages, 3 figures. Accepted to EMNLP 2026 Industry Track
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Concrete Problems in AI Safety
- Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
- MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
- FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
- TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
- PostTrainBench: Can LLM Agents Automate LLM Post-Training?
- DeployBench: Benchmarking LLM Agents for Research Artifact Deployment
- RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
- Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference
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