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Research questionHow can coding and terminal agents be post-trained without distorting production-faithful token flows and control operations?Simplified training environments and reconstructed logs can change the prompts and token flows encountered in deployment. They can also mix control operations with ordinary model activity, causing training and evaluation to reflect the wrong execution process.
AI Agents
Code Generation & Program Synthesis
Evaluation & Benchmarks
LLM Pretraining & Post-training
Latest papersRecent research connected to this question, newest first.Train What You Deploy:Token-Faithful Post-Training of a Production CodingThe source develops a fidelity-aware training pipeline and certification-based policy optimization for coding and terminal agents. Evidence comes from matched Baize5B and Baize10B evaluations on TMax-100 and training-pipeline audits, so it does not establish broader performance or generalization.research paper · Sep 4, 2026
Related questions
How can terminal-agent training environments stay challenging as models improve without costly on-policy synthesis?How can long-horizon LLM agents preserve answer quality under tight prompt-token budgets?How can recorded terminal-agent trajectories be converted into executable, varied training environments?How sparse can token-level supervision be in on-policy post-training without weakening language-model reasoning?