chain-of-thought

Arcee AI released Trinity-Large-Thinking, a 398B-parameter sparse Mixture-of-Experts model with approximately 13B active parameters per token, post-trained with extended chain-of-thought reasoning for agentic workflows. The model achieves 94.7% on τ²-Bench, 91.9% on PinchBench, and 98.2% on LiveCodeBench, generating explicit reasoning traces in <think>...</think> blocks before producing responses.

Researchers from Alibaba's Qwen team and Tsinghua University developed HopChain, a framework that automatically generates multi-step image questions to fix how vision-language models fail during complex reasoning tasks. The method improved 20 out of 24 tested benchmarks by forcing models to re-examine images at each reasoning step, preventing early perceptual errors from cascading through subsequent steps.

Alibaba's Qwen team has developed Future-KL Influenced Policy Optimization (FIPO), a training algorithm that assigns different weights to tokens based on their influence on subsequent reasoning steps, rather than treating all tokens equally. Testing on Qwen2.5-32B-Base showed reasoning chains double from ~4,000 to 10,000+ tokens, with AIME 2024 accuracy improving from 50% to 58%, outperforming Deepseek-R1-Zero-Math-32B (47%) and OpenAI's o1-mini (56%). The team plans to open-source the system.

DeepSeek has released DeepSeek-R1, a text generation model featuring reasoning capabilities through chain-of-thought processing. The model was published January 20, 2025 and has accumulated over 830,000 downloads on Hugging Face.

A new Bytedance study reveals that large reasoning models actually know when they've reached the correct answer, but common sampling methods prevent them from stopping. The models engage in unnecessary cross-checking and reformulation despite already solving problems correctly.