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ARC Prize publishes Meta Muse Spark Open's evaluation results on ARC-AGI
ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).
ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-
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ARC Prize tests Meta Spark Open: ARC-AGI-2 score 57.5%, cost about 9 times that of models with the same score
ARC Prize tested Meta Spark Open on ARC-AGI: it scored 89.0% on ARC-AGI-1, 57.5% on ARC-AGI-2, and only 0.4% on ARC-AGI-3. Its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs. Across providers on OpenRouter, the model costs $1.25/M for input and $4.25/M for output, while Haiku 5.5 and GPT-6 Luna both cost $0.10/M for input and $0.50/M for output. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).
ARC Prize tested Meta Spark Open on ARC-AGI: it scored 89.0% on ARC-AGI-1, 57.5% on ARC-AGI-2, and only 0.4% on ARC-AGI-3. Its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs. Across providers on OpenRouter, the model costs $1.25/M for input and $4.25/M for output, while Haiku 5.5 and GPT-6 Luna both cost $0.10/M for input and $0.50/M for output. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to
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ARC Prize announces Meta Muse Spark Open's evaluation results on various versions of ARC-AGI
ARC Prize announced the validation results of Meta Muse Spark Open on ARC-AGI: ARC-AGI-1 is 89.0% at $0.29 per task, ARC-AGI-2 is 57.5% at $0.73 per task, and ARC-AGI-3 is 0.4% at $3.0K under the provider adapter harness and 0.1% at $3.9K under the standard harness. ARC Prize states that its scores on v1/v2 are similar to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens, while Haiku 5.5 and GPT-6 Luna are both $0.10 per million input tokens and $0.50 per million output tokens. On public tasks, as the reasoning scale increases, the ARC-AGI-2 score rises from an average of 55k tokens/task corresponding to 29.4% at the medium tier, to 150k corresponding to 51.0% at the high tier, and then to 180k corresponding to 57.2% at the xhigh tier; ARC-AGI-1 has already reached 92.8% at the high tier and rises to 94.0% at the xhigh tier.
ARC Prize announced the validation results of Meta Muse Spark Open on ARC-AGI: ARC-AGI-1 is 89.0% at $0.29 per task, ARC-AGI-2 is 57.5% at $0.73 per task, and ARC-AGI-3 is 0.4% at $3.0K under the provider adapter harness and 0.1% at $3.9K under the standard harness. ARC Prize states that its scores on v1/v2 are similar to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million o
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Elorian Releases DecoyCount Benchmark: Frontier Models All Score Below 50% on Visual Counting Accuracy
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each took the average of three runs. Elorian CEO Andrew Dai said the benchmark examines both vision and reasoning, and can distinguish whether a model failed to see the objects or miscounted them.
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each to
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Amazon SageMaker AI Multi-Turn Reinforcement Learning Practical Guide: Training gpt-oss-20b with a Wordle Example
AWS has published a practical guide for multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. Using Wordle as an example, it deploys the agent and reward function to Amazon Bedrock AgentCore, and the training job updates LoRA adapters via GRPO. In the example, gpt-oss-20b at default reasoning effort improved its solve rate on held-out words from 61% to 84%, at a training token cost of about $339 and an AgentCore runtime cost of about $79. The article also details failures such as two inverted reward design errors, token invalidation under concurrency, and tool calls degrading into plain text, and recommends evaluating under the decoding settings used in final serving.
AWS has published a practical guide for multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. Using Wordle as an example, it deploys the agent and reward function to Amazon Bedrock AgentCore, and the training job updates LoRA adapters via GRPO. In the example, gpt-oss-20b at default reasoning effort improved its solve rate on held-out words from 61% to 84%, at a training token cost of about $339 and an AgentCore runtime cost of about $79. The article also details failures such as two
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DeepSeek Search ranks 17th on the Artificial Analysis Search Index
DeepSeek Search scores 69 on the Artificial Analysis Search Index, ranking 17th among 27 search products, and is the lowest-priced paid search API on the list. It charges $0.75 per 1k searches, with a per-task cost of about $0.032, ranking 5th cheapest among 27 products. On AA-Omniscience its accuracy is 70%, ranking 10th, but on BrowseComp, which requires multi-hop reasoning, it is only 63%, ranking 25th; each search returns about 7k tokens, raising model cost to the 2nd highest on the list.
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i14 Trains and Deploys the Laplace 3B Linear Attention Model on AWS Trainium2 Using Native PyTorch
i14 partnered with AWS to train and deploy Laplace 3B, based on Gated Linear Attention, on AWS Trainium2 using TorchNeuron Native. A single trn2.48xlarge achieves a training throughput of about 120,000 tokens/s. The model's recurrent state per sequence is fixed at 29.5 MB and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication to cut per-GPU communication volume by 24x and raise training throughput by 1.63x.
i14 partnered with AWS to train and deploy Laplace 3B, based on Gated Linear Attention, on AWS Trainium2 using TorchNeuron Native. A single trn2.48xlarge achieves a training throughput of about 120,000 tokens/s. The model's recurrent state per sequence is fixed at 29.5 MB and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all to all communication to cut per-GPU communication volume by 24x and raise training
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Stanford CME 295 Lecture 4 released: Teaching reinforcement learning with LLMs
The Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, Reinforcement learning with LLMs, and both the video and lecture notes are now public. This lecture goes from RL and Reward Design to PPO, GRPO, RLVR, and DAPO, explaining the notation in each one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention inference optimization, MCP and AI Agents, and diffusion language models.
The Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, Reinforcement learning with LLMs, and both the video and lecture notes are now public. This lecture goes from RL and Reward Design to PPO, GRPO, RLVR, and DAPO, explaining the notation in each one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention infere
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