Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant barriers to real-world deployment, including substantial computational overhead, memory footprint, and inference latency. While model pruning presents a viable solution to these challenges, existing unstructured pruning techniques often yield irregular sparsity patterns that necessitate specialized hardware or software support. In this work, we explore structured pruning, which eliminates entire architectural components and maintains compatibility with standard hardware accelerators. We introduce a novel structured pruning framework that leverages a hybrid multi-domain calibration set and an iterative calibration strategy to effectively identify and remove redundant channels. Extensive experiments on various models across diverse downstream tasks show that our approach achieves significant compression with minimal performance degradation.

Group-relative policy optimization (GRPO, DAPO, ARPO) has become the dominant recipe for RL fine-tuning of reasoning LLMs, yet in the resource-constrained regime most practitioners train in (a single GPU, small batches) it consistently suffers from rapid entropy collapse that kills exploration, together with sparse credit assignment in long rollouts, which is most extreme under multi-turn tool use but already present in long single-turn chain-of-thought. We address both failure modes with JANUSPO, a drop-in GRPO modification that introduces asymmetric PPO clipping 𝜖₊<𝜖_- to tolerate larger downward than upward updates, pairs it with an asymmetric KL schedule 𝛽_pos<𝛽_neg that doubles as a free entropy regulator without any explicit upper bound, and redistributes the terminal reward through a turn-weighted decomposition w_t(n) that generalises naturally from single-turn CoT to multi-turn rollouts. On Qwen2.5-3B-Instruct / GSM8K, JANUSPO improves from 74.8% to 84.3±0.2% (mean±std over 2 seeds) across 14 controlled ablations, and the same recipe lifts 7B from 85.3% to 93.6% with no hyper-parameter retuning. On multi-turn DeepSearch (103 GAIA + 100 HLE), SFT-warmup-then-JANUSPO is the only RL recipe whose paired comparison against SFT survives best@8 outlier verification, achieving strict dominance on all four reward and F1 columns while a matched-init GRPO baseline’s apparent F1 wins collapse to 0%. Beyond this main result, we document a systematic negative finding: SFT warm-up is a necessary precondition for GRPO-style multi-turn RL on format-unaware base models.