Qwen3-8B

This technical report explores how the placement of LoRA adapters within Transformer layers affects multi-hop reasoning capabilities. It concludes that targeting early layers is critical for preventing models from 'stopping thinking' too early during complex inference loops.

Researchers have developed FlyBy, a framework that allows small language models to overcome knowledge limitations by selectively querying stronger external models. This approach optimizes test-time computation by distinguishing between problems solvable through more thought and those requiring new information.

FlowBalance is a novel reinforcement learning method designed to stabilize the self-improvement process in large reasoning models. It effectively integrates sparse verifier feedback with dense, privileged-hindsight self-guidance to prevent model overconfidence and improve mathematical reasoning accuracy.

AgentGrad is a novel prompt optimization framework for multi-agent systems that improves both the accuracy and efficiency of textual gradient methods. By using sequential intervention to target specific agents and semantic abstraction to group similar errors, it outperforms current state-of-the-art approaches.

The paper introduces TTPO, a method designed to optimize LLM reasoning during test-time training by utilizing asymmetric learning signals from pseudo-labels. It effectively mitigates the risks of noisy majority-vote labels by distilling agreeing rollouts and penalizing disagreeing ones.

The paper introduces TTPO, a method designed to optimize LLM reasoning during test-time training by utilizing asymmetric learning signals from pseudo-labels. It effectively mitigates the risks of noisy majority-vote labels by distilling agreeing rollouts and penalizing disagreeing ones.