Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning

2026-08-17

AI Summary

This study addresses the performance-safety imbalance in enterprise-level agent tool calling by proposing an Anchored Supervised Fine-Tuning strategy. Built upon a Mixture-of-Experts architecture, this approach integrates a hybrid Muon-Adam optimizer with Kullback-Leibler divergence constraints, leveraging synthetic trajectory data to achieve stable alignment under few-shot conditions while enhancing tool-use capabilities and ensuring safety. Experimental results demonstrate that the model achieves a score of 0.785 on BFCL Core and attains the highest average across six benchmarks. Notably, it significantly outperforms its predecessor in Writer Agent tasks while maintaining superior performance in safety and bias evaluations. Collectively, these findings establish a novel paradigm for the efficient and secure deployment of enterprise intelligent agents.

Abstract

Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model with Anchored Supervised Fine-Tuning on a compact corpus of verified, synthetic tool-use trajectories, optimized with a Muon + Adam hybrid. The recipe is deliberately conservative and deliberately controlled: 626 trajectories, a single epoch, a low learning rate, and a KL anchor to the frozen base. The model shows substantial gains over the previous default model for Writer Agent, and compares favorably with several recent models on public benchmarks, scoring the highest on BFCL Core at $0.785$ and posts the highest six-benchmark mean of the cohort. Furthermore, the model has shown itself to be competitive or leading relative to comparators in our bias and safety evaluations.

Problem

• Agentic Tasks
• Tool Use
• Large Language Model
• Enterprise AI

Innovation

• Anchored Supervised Fine-Tuning
• Synthetic Tool-Use Trajectories
• Mixture-of-Experts
• Muon + Adam Hybrid Optimizer
• Agentic Tasks