Parameter Efficient Reasoning Through Rl Scaling

via “reasoning and multi-step problem solving”

Microsoft's 3.8B model with 128K context for edge deployment.

Unique: Achieves 69% MMLU reasoning performance in a 3.8B model through synthetic training data specifically designed for reasoning patterns, significantly outperforming typical SLMs on reasoning benchmarks despite extreme parameter efficiency

vs others: Delivers reasoning capability in 3.8B parameters (vs. Mistral 7B, Llama 3.2 1B which don't emphasize reasoning) while remaining mobile-deployable, trading some accuracy for extreme efficiency and edge compatibility

via “high-efficiency reasoning via data-quality-optimized transformer”

Microsoft's 14B model rivaling 70B through data quality.

Unique: Achieves 70B-class reasoning performance at 14B parameters through data curation rather than scale — training philosophy inverts the typical LLM scaling law by prioritizing synthetic and filtered dataset quality over raw parameter count and training tokens

vs others: Outperforms Llama 2 70B and Mistral 7B on reasoning benchmarks while using 5x fewer parameters than Llama 2, enabling faster inference and lower deployment costs than larger models with comparable reasoning capability

via “reasoning and multi-step problem decomposition”

TII's 180B model trained on curated RefinedWeb data.

Unique: Achieves strong reasoning performance through scale (180B parameters) and data quality (3.5T meticulously-cleaned RefinedWeb tokens) rather than specialized reasoning fine-tuning, enabling emergent reasoning capabilities across diverse domains without task-specific training.

vs others: Larger parameter count than reasoning-specialized models like Llama 2 70B enables better few-shot reasoning, but lacks explicit chain-of-thought fine-tuning that models like GPT-4 or Claude employ, potentially requiring more sophisticated prompting to achieve comparable reasoning quality.

via “parameter-efficient reasoning through rl scaling”

Alibaba's 32B reasoning model with chain-of-thought.

Unique: Achieves reasoning performance comparable to 671B-parameter models through RL scaling on robust foundation models with outcome-based verification, demonstrating parameter-efficient reasoning through training approach rather than architectural compression

vs others: Delivers reasoning capability at 32B parameters competitive with 671B+ parameter models through RL training efficiency, enabling cost-effective and resource-efficient reasoning deployment compared to larger models

via “extended-chain-of-thought reasoning with configurable compute allocation”

OpenAI's most powerful reasoning model for complex problems.

Unique: Implements variable-depth reasoning with explicit user-controlled compute budgets rather than fixed token limits, enabling dynamic allocation across problem complexity — users can specify reasoning intensity (low/medium/high) and the model adapts internal chain-of-thought depth accordingly

vs others: Outperforms GPT-4 and Claude on ARC-AGI (87.5% vs ~85%) by allocating more reasoning compute to genuinely hard problems rather than uniform token budgets, and provides explicit cost-quality controls that competitors lack

via “multi-level reasoning with configurable compute budgets”

Cost-efficient reasoning model with configurable effort levels.

Unique: Implements learned routing at inference time to dynamically allocate reasoning compute across three effort levels without requiring separate model checkpoints, enabling cost-performance tradeoffs within a single model call rather than requiring model selection

vs others: Offers finer cost control than o1 (which has fixed reasoning depth) and lower cost than o3 while maintaining comparable reasoning quality on STEM tasks through adaptive compute allocation

via “cost-optimized inference with dynamic reasoning depth”

Latest compact reasoning model with native tool use.

Unique: Implements automatic complexity-based reasoning budget allocation via a pre-inference classifier, reducing costs for simple problems without sacrificing quality on complex ones. This differs from fixed-reasoning-depth models (o1/o3) and non-reasoning models (GPT-4o) which don't adapt reasoning investment.

vs others: More cost-efficient than o1/o3 for mixed workloads (estimated 30-50% cost reduction for typical applications) while maintaining reasoning quality; more capable than GPT-4o on complex problems while being cheaper on simple ones.

via “adaptive-thinking-complexity-aware-reasoning”

Anthropic's most intelligent model, best-in-class for coding and agentic tasks.

Unique: Implements learned complexity routing that estimates problem difficulty from input tokens alone, without requiring explicit user hints or metadata. This is distinct from static reasoning budgets (o1, o1-mini) by dynamically allocating compute per-request based on inferred task characteristics, reducing wasted reasoning on trivial queries.

vs others: More efficient than fixed-reasoning-budget competitors by automatically scaling reasoning effort to task complexity, and more transparent than black-box reasoning models by still exposing thinking tokens when needed for debugging.

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 “logical-reasoning-and-formal-inference”

INTELLECT-3 is a 106B-parameter Mixture-of-Experts model (12B active) post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL). It offers state-of-the-art performance for its size across math,...

Unique: RL post-training optimizes for logical consistency and formal correctness in reasoning traces; uses chain-of-thought patterns that decompose inference into verifiable steps rather than end-to-end black-box reasoning

vs others: Produces more transparent and verifiable reasoning than single-step models while maintaining efficiency through MoE routing that activates only reasoning-specific experts

via “extended-chain-of-thought reasoning with token budget allocation”

Olmo 3 32B Think is a large-scale, 32-billion-parameter model purpose-built for deep reasoning, complex logic chains and advanced instruction-following scenarios. Its capacity enables strong performance on demanding evaluation tasks and...

Unique: Olmo 3 32B Think implements reasoning-focused inference at 32B parameters using an internal thinking budget mechanism, making it one of the few open-source models with explicit reasoning-phase architecture rather than relying solely on prompt-based CoT. The model is trained with reasoning supervision, enabling it to learn when and how to allocate computation to hard problems.

vs others: Smaller and more accessible than OpenAI's o1 (which is closed-source and expensive) while maintaining reasoning capabilities; faster inference than larger reasoning models like Llama 3.1 405B, making it practical for production systems with latency constraints

via “configurable-reasoning-effort-modes”

Seed-2.0-mini targets latency-sensitive, high-concurrency, and cost-sensitive scenarios, emphasizing fast response and flexible inference deployment. It delivers performance comparable to ByteDance-Seed-1.6, supports 256k context, four reasoning effort modes (minimal/low/medium/high), multimodal und...

Unique: Exposes reasoning effort as a first-class API parameter with four discrete levels, each with predictable compute/latency/quality trade-offs. This differs from models like o1 that use fixed reasoning budgets; Seed-2.0-mini allows per-request tuning without model switching.

vs others: Provides more granular reasoning control than Claude 3.5 Sonnet (which has no reasoning effort parameter) while maintaining lower latency than o1-mini by using lightweight chain-of-thought instead of full tree-search by default.

via “extended reasoning with implicit chain-of-thought”

Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not...

Unique: Implicit reasoning allocation based on problem complexity, with reasoning traces integrated into output without explicit token budget management, contrasting with OpenAI's explicit reasoning token approach

vs others: More transparent reasoning than GPT-4o (which hides reasoning) but less controllable than o1 (which offers explicit reasoning token budgets); better for exploratory reasoning where depth is problem-dependent

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 “logical reasoning and problem-solving with step-by-step decomposition”

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...

Unique: Instruction-tuning explicitly optimizes for chain-of-thought reasoning patterns, enabling the model to articulate intermediate steps and self-correct. 70B scale provides sufficient capacity for multi-step reasoning without losing coherence.

vs others: Better reasoning transparency than smaller models and comparable to GPT-4 on many reasoning tasks at lower cost, though specialized reasoning models or symbolic solvers may outperform on highly constrained domains like formal mathematics.

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 “inference-time token scaling for adaptive reasoning depth”

OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...

Unique: Implements reasoning depth as a runtime parameter that scales internal computation without prompt changes, using inference-time token allocation rather than prompt engineering or model switching. This is architecturally distinct from approaches like few-shot prompting or chain-of-thought prompting, which require explicit prompt modification.

vs others: More efficient than prompt engineering for controlling reasoning depth; avoids prompt bloat and token waste from explicit chain-of-thought instructions; enables dynamic adjustment per-request without recompiling prompts.

via “general reasoning with structured output”

MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...

Unique: Embeds chain-of-thought reasoning patterns directly in model weights through training on reasoning-heavy datasets, enabling multi-step decomposition without requiring external prompting frameworks or specialized reasoning APIs

vs others: Delivers reasoning capabilities at 10B active parameters comparable to 70B dense models through expert routing, reducing inference cost by 60-70% while maintaining structured output compatibility

via “extended-chain-of-thought reasoning with compute allocation”

The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently...

Unique: Uses RL-trained thinking mechanism that allocates compute dynamically across reasoning phases, enabling multi-path exploration and self-correction within a single forward pass. Unlike standard LLMs that generate responses directly, o3-pro separates thinking tokens from output tokens, allowing explicit control over reasoning depth via API parameters.

vs others: Outperforms GPT-4 and Claude 3.5 on complex reasoning benchmarks (AIME, MATH, coding competitions) by 15-40% due to RL-optimized thinking, but costs 3-5x more per request and requires longer latency tolerance.