bfcl-v3-61a8963a·3 events·first seen Aliases: BFCL-V3, BFCL-V4, BFCL-v4
Mach-Mind-4-Flash is a 35B-parameter Mixture-of-Experts model with only 3B activated parameters that achieves performance comparable to 100B-class models through post-training techniques alone. The pipeline combines a unified RL/OPD training infrastructure with multi-teacher scheduling, parallel domain-specific RL experts fused via Multi-Teacher On-Policy Distillation (MOPD), and Hybrid Median-length Policy Optimization (HMPO) which compresses reasoning chains 19-46% with minimal accuracy loss. Benchmark results include 92.70 on AIME'26, 82.82 on IFBench, and 75.80 on BFCL-v4, claiming to lead or match models 10-30x its activated size at a fraction of inference cost. The work is notable for demonstrating that post-training optimization can close large gaps in activated parameter count for agentic tasks.
CurateEvo is a new framework for agentic post-training that treats data curation as a dynamic, evolving process rather than a fixed preprocessing step. The system represents curation strategies as executable code and iteratively rewrites them based on failed trajectories from a held-out development set, producing SFT data, RL data, and an inference-time memory bank. Evaluated on ACEBench-Agent, BFCL-V4, and τ²-Bench, CurateEvo outperforms prior curation methods by 3.2 and 2.7 average points in labeled and wild-data settings respectively, while also reducing curation overhead.
Researchers present KATE (Knowledge-Augmented Tool Execution), a framework addressing LLM failures in multi-step tool use by systematically studying knowledge acquisition, activation, and internalization. Key findings include that instance-level experiential knowledge outperforms abstract intent-level knowledge, that expanding reasoning width via parallel sampling with aggregation beats deeper chain-of-thought, and that reinforcement learning outperforms supervised fine-tuning for knowledge internalization. KATE is evaluated on BFCL-V3 and AppWorld benchmarks, showing consistent improvements over strong baselines across model scales.