Reinforcement Learning Optimized Chain Of Thought Reasoning

via “chain-of-thought-multi-stage-reasoning”

Google's vision-language-action model for robotics.

Unique: Integrates chain-of-thought reasoning directly into the action generation pipeline by representing both reasoning steps and actions as text tokens, allowing the same transformer to generate interpretable intermediate steps and grounded robot actions

vs others: Provides interpretability and reasoning transparency that black-box policy networks lack, while avoiding separate symbolic reasoning systems by leveraging the language model's native ability to generate and process reasoning text

via “chain-of-thought reasoning with reinforcement learning optimization”

text-generation model by undefined. 38,71,385 downloads.

Unique: Uses RL-based training to learn dynamic reasoning token allocation per problem, making reasoning depth adaptive rather than fixed; explicitly optimizes for reasoning quality via reward signals rather than implicit capability from instruction tuning

vs others: Outperforms GPT-4 and Claude on AIME/MATH benchmarks by learning to allocate reasoning compute efficiently, while remaining open-source and deployable locally without API dependencies

via “reasoning and problem decomposition with chain-of-thought patterns”

This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus). The model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-...

Unique: Inherits Claude's explicit chain-of-thought training approach, which emphasizes showing reasoning work as part of the output rather than reasoning internally, making reasoning patterns visible and auditable

vs others: More transparent reasoning than models without explicit chain-of-thought training, but less specialized than models fine-tuned specifically on mathematical reasoning datasets or formal logic

via “reasoning and chain-of-thought decomposition”

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Unique: Reasoning capability emerges from instruction-tuning on datasets containing reasoning examples, not explicit reasoning modules or symbolic reasoning engines. The model learns to generate plausible reasoning chains through imitation, making it flexible but not formally verifiable.

vs others: Provides comparable chain-of-thought quality to GPT-4 on most reasoning tasks while using 3x fewer active parameters, though may require more explicit prompting to trigger reasoning compared to larger models.

via “extended-reasoning-chain-of-thought-generation”

ERNIE-4.5-21B-A3B-Thinking is Baidu's upgraded lightweight MoE model, refined to boost reasoning depth and quality for top-tier performance in logical puzzles, math, science, coding, text generation, and expert-level academic benchmarks.

Unique: Uses proprietary A3B (Adaptive Attention-Based Branching) mechanism that dynamically allocates compute across reasoning paths rather than fixed-depth chains, enabling adaptive reasoning depth based on problem complexity. This differs from static chain-of-thought approaches by treating reasoning as a branching tree with learned pruning heuristics.

vs others: Outperforms GPT-4 and Claude on mathematical reasoning benchmarks while maintaining 21B parameter efficiency through MoE architecture, making it faster and cheaper for reasoning-heavy workloads than larger closed-source models

via “chain-of-thought reasoning with explicit step decomposition”

Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...

Unique: Constitutional AI training enables natural reasoning articulation without explicit chain-of-thought prompting, producing coherent reasoning traces that reflect actual model decision-making rather than post-hoc rationalization

vs others: Reasoning quality and naturalness exceed GPT-4's chain-of-thought due to instruction tuning specifically for reasoning transparency, producing more interpretable intermediate steps

via “reasoning and chain-of-thought decomposition”

Mistral Large 2 2411 is an update of [Mistral Large 2](/mistralai/mistral-large) released together with [Pixtral Large 2411](/mistralai/pixtral-large-2411) It provides a significant upgrade on the previous [Mistral Large 24.07](/mistralai/mistral-large-2407), with notable...

Unique: Mistral Large 2411 implements implicit chain-of-thought through training on reasoning-heavy datasets, enabling natural step-by-step decomposition without explicit prompting while maintaining efficiency through optimized token generation

vs others: Provides reasoning quality comparable to GPT-4 while maintaining lower latency and cost through more efficient token usage

via “extended-chain-of-thought-generation”

Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either...

Unique: Combines 70B parameter scale with process-reward modeling to maintain reasoning coherence across 10+ step chains, whereas smaller models typically degrade after 3-4 steps due to context drift and accumulated errors

vs others: Produces more reliable multi-step reasoning than GPT-3.5 while being more cost-effective than GPT-4 for reasoning tasks, with explicit step visibility that proprietary models don't expose

via “reasoning chain decomposition and step-by-step problem solving”

Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.

Unique: Implements chain-of-thought reasoning through prompt-based guidance rather than architectural modifications, enabling flexible reasoning depth control without model retraining

vs others: More cost-effective than specialized reasoning models (o1) for moderate complexity problems; produces transparent reasoning vs black-box outputs; trades off reasoning depth vs cost and latency

via “complex reasoning and chain-of-thought decomposition”

Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning...

Unique: Command R7B's reasoning is optimized for RAG and tool-use contexts, where intermediate steps can reference retrieved documents or tool outputs, enabling grounded reasoning that combines external knowledge with logical inference

vs others: Outperforms GPT-4 on MATH and AIME benchmarks when combined with tool use for calculation, because it can delegate computation to tools rather than attempting symbolic math in-context

via “reasoning-focused problem decomposition and chain-of-thought”

This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....

Unique: Trained specifically on chain-of-thought datasets to prioritize reasoning steps, using attention mechanisms that weight intermediate reasoning tokens higher than direct answers, enabling more transparent problem-solving

vs others: Comparable to GPT-4's reasoning on complex problems, while maintaining lower latency and cost; outperforms Llama 2 on multi-step reasoning due to larger parameter count and specialized training

via “reasoning and step-by-step problem solving”

Olmo 3.1 32B Instruct is a large-scale, 32-billion-parameter instruction-tuned language model engineered for high-performance conversational AI, multi-turn dialogue, and practical instruction following. As part of the Olmo 3.1 family, this...

Unique: Instruction-tuning on chain-of-thought datasets enables the model to generate coherent reasoning steps when prompted, without requiring explicit reasoning modules or external symbolic solvers — this implicit reasoning approach is more flexible than hard-coded reasoning systems but less precise than specialized solvers

vs others: More transparent reasoning than direct answer generation, but lower accuracy on specialized domains than models fine-tuned exclusively on reasoning tasks; better for educational use cases than production problem-solving

via “chain-of-thought reasoning with explicit step decomposition”

GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...

Unique: Implements chain-of-thought as a first-class reasoning pattern with architectural support for maintaining reasoning coherence across long inference chains, enabling transparent multi-step problem solving

vs others: Produces more reliable reasoning than GPT-4o on complex problems because it maintains reasoning context better across longer chains and has been optimized specifically for instruction following in reasoning tasks

via “extended-reasoning-chain-of-thought-generation”

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

Unique: Uses large-scale reinforcement learning (not just supervised fine-tuning) to train the model to dynamically allocate internal computation time based on problem difficulty, with an opaque but learned reasoning process that explores multiple solution paths before responding. This differs from standard models that apply fixed computation per token.

vs others: Outperforms GPT-4 and Claude on math, coding, and formal reasoning benchmarks by 10-30% due to learned reasoning allocation, but trades latency and cost for accuracy on hard problems.

via “chain-of-thought reasoning with reinforcement learning optimization”

Alibaba's QWQ — advanced reasoning model with improved math/logic capabilities

Unique: Uses RL-optimized reasoning rather than prompt-engineering-based chain-of-thought — the model's weights are trained to naturally decompose problems, not instructed to do so via prompting. This enables more robust reasoning on novel problem types compared to models that only learn reasoning patterns from supervised examples.

vs others: Offers competitive reasoning performance to DeepSeek-R1 and o1-mini while remaining fully open-source and runnable locally, eliminating API dependency and cost for reasoning workloads.

via “reasoning and chain-of-thought decomposition”

GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...

Unique: Learns chain-of-thought patterns from training data rather than using explicit prompting tricks, enabling more natural and flexible reasoning decomposition that adapts to problem complexity without manual prompt engineering

vs others: More reliable reasoning than GPT-3.5 Turbo and comparable to GPT-4o on hard problems, while maintaining lower latency through architectural efficiency rather than brute-force scaling

via “semantic reasoning and chain-of-thought explanation”

The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to December 2023.

Unique: Implements learned chain-of-thought patterns from training data rather than using external reasoning frameworks, producing natural language reasoning that mirrors human problem-solving without requiring separate symbolic reasoning engines

vs others: More natural and interpretable reasoning chains than symbolic reasoners, but less formally verifiable; outperforms Claude 3 on mathematical reasoning benchmarks due to larger training dataset on math problems

via “reasoning and step-by-step problem decomposition with chain-of-thought prompting”

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...

Unique: Implements chain-of-thought reasoning through instruction-tuning patterns rather than specialized reasoning architectures or reinforcement learning, enabling reasoning capabilities without model retraining or inference-time search

vs others: Faster reasoning than models requiring inference-time search or tree-of-thought exploration, while maintaining better explainability than black-box models; lower cost than specialized reasoning models like o1 for problems not requiring deep search

via “semantic-reasoning-with-chain-of-thought-decomposition”

GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...

Unique: Combines chain-of-thought reasoning with adaptive computation allocation, enabling transparent reasoning that automatically allocates more tokens to complex steps

vs others: More efficient reasoning than GPT-4 Turbo due to adaptive allocation, and more transparent than Claude 3.5 Sonnet for step-by-step problem decomposition

via “reasoning-chain generation with step-by-step problem decomposition”

DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations...

Unique: Instruction-tuned on 15 trillion tokens to reliably generate explicit reasoning chains without requiring special prompting techniques, whereas most models require careful chain-of-thought prompt engineering to produce transparent reasoning. Demonstrates stronger reasoning consistency across diverse problem types.

vs others: More reliable reasoning traces than GPT-3.5 and comparable to GPT-4, but with lower latency and cost; however, OpenAI's o1 model provides superior reasoning on complex mathematical and scientific problems through reinforcement learning on reasoning quality