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  "lastUpdatedAt": "2026-04-29T20:21:48Z",
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    "title": "deprecated",
    "source": "llm",
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      "Subnet metadata is labeled 'deprecated' and price is down -67% over 30 days, strongly indicating the subnet is in the process of being abandoned or repurposed despite ongoing repo commits"
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    "score": 44,
    "summary": "Subnet 81 hosts 'grail', a verifiable post-training framework for LLMs built on reinforcement learning principles, despite the subnet metadata being labeled 'deprecated'. The network is functionally near-dormant with only 1 active miner and a -67% price collapse over 30 days. Development activity remains surprisingly high with 59 releases in 12 months, creating a sharp disconnect between code velocity and network health.",
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      "Strong commit velocity with 47 commits in the last 30 days and 184 in 90 days, showing sustained development effort",
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    "durability": "watch",
    "recommendation": "Hold off on any position until the active miner count recovers above 10+ and the 'deprecated' metadata is resolved \u2014 current network state does not support meaningful subnet participation despite healthy code velocity.",
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      "text": "The ADHD-maxing you have to do to manage 10 Claude code sessions at the same time is insane.",
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      "text": "Pretty wild to see our work on PULSE show up in a real 1T-scale post-training run done by @cursor_ai.\n\nCursor built Composer 2 in collaboration with Fireworks and trained it across multiple datacenters, getting huge savings by syncing only the weights that actually changed between RL checkpoints. Fireworks reports that more than 98% of BF16 weights can stay bit-identical from one checkpoint to the next, and they cited our paper on this, too.\n\nThat is basically the exact sparsity pattern we showed in our paper, where we introduced PULSE, a lossless method for 100x more efficient weight-sync communication for RL training. Their system is very close to this idea in practice: exploiting the fact that only a tiny fraction of weights actually change between RL steps.\n\nThe deeper reason for this is not that RL gradients are sparse. They are not. The gradients are still dense. What becomes sparse is the realized weight update. In RL, learning rates are tiny, and with Adam, the update size stays bounded around the learning rate. Then BF16 adds a hard threshold: if the update is too small relative to the weight, it just rounds away, and the stored weight does not change at all. So from one checkpoint to the next, most of the model literally stays identical.\n\nThat is why this is such a useful systems idea. Lower precision, like using BF16, does not just save compute. It can also save communication, because more tiny updates get absorbed and fewer weights need to be shipped. At that point, compute efficiency and comms efficiency stop being a tradeoff. They start reinforcing each other.\n\nIf you want the deeper story on why RL updates get this sparse, the theory behind it, and how to push weight-sync bandwidth down by 100x+, take a look at our paper:\nhttps://t.co/ncVP8EN9lR\n\nThe Fireworks blog on Composer 2 that cited our work:\nhttps://t.co/HR7u9wPLhJ\n\nThe animation is taken from Fireworks!",
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      "text": "\u26a1 Weekly @covenant_ai / @grail_ai update!\n\nWe kept pushing the frontier of verified inference rollouts in an open, adversarial setting.\n\nThis week brought upgrades to verification precision, miner debugging tools, and stronger defenses against copy-cats. What dropped 1/9 \ud83e\uddf5\ud83d\udc47",
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      "text": "@bgyankarki is one of the awesome founders I know. Make sure to check out his cool product at @thedriveAI",
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      "text": "@bgyankarki is one of the awesome founders I know. Make sure to check out his startup @thedriveAI \ud83e\udd18",
      "created_at": "2025-08-08T17:34:39-03:00",
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      "text": "It\u2019s crazy how the demand for training coding models with RL has exploded in just a few months. \n\nPeople from finance to IT are literally throwing \ud83d\udcb0 at me! Everybody wants their own specialized coding model now.\n\nWild compared to just a few months ago.",
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      "text": "RT @novasarc01: the mit 6.S184 lectures on flow matching and diffusion are really helpful for those who want to start with flow matching an\u2026",
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      "handle": "erfan_mhi",
      "text": "I\u2019m thinking along the same lines these days. If anybody wants to collaborate/brainstorm on this let me know.",
      "created_at": "2025-05-14T16:22:57-03:00",
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      "handle": "erfan_mhi",
      "text": "The original DeepSeekMath (GRPO) paper already hinted at this (see section 5.2.2). Great to see a deeper dive now! My take: current RL methods overlook a crucial piece: the exploration/exploitation tradeoff. [\ud83e\uddf5\ud83d\udcf7]  \n\n1/6",
      "created_at": "2025-04-22T12:35:15-03:00",
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      "text": "RT @RichardSSutton: David Silver really hits it out of the park in this podcast. The paper \"Welcome to the Era of Experience\"  is here: htt\u2026",
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      "handle": "erfan_mhi",
      "text": "If reinforcement learning models CAN cheat their way to maximizing the return, they WILL cheat!",
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      "handle": "erfan_mhi",
      "text": "RT @Alibaba_Qwen: Today, we release QwQ-32B, our new reasoning model with only 32 billion parameters that rivals cutting-edge reasoning mod\u2026",
      "created_at": "2025-03-05T18:56:32-03:00",
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      "tweet_id": "1897323713177248011",
      "handle": "erfan_mhi",
      "text": "The head of my MSc lab just won the Turing Award \ud83c\udf89",
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      "tweet_id": "1886498499899854888",
      "handle": "erfan_mhi",
      "text": "I just gave one of the hardest interview questions I had a year ago to @OpenAI o3-mini-high and it solved it. Damn.",
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      "text": "RT @arankomatsuzaki: SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training\n\nShows that:\n- RL generalizes in\u2026",
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      "handle": "erfan_mhi",
      "text": "Seeing this pattern everywhere\u2014learning on reasoning &amp; coding tasks noticeably boosts math reasoning, sometimes even more than direct fine-tuning on math itself. Transformer\u00b2 shows this too even tho they're not doing common fine-tuning. Transfer learning at its peak I guess.",
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      "handle": "erfan_mhi",
      "text": "Reinforcement learning once shook world politics, especially in China, with AlphaGo, and now ~9 years later, DeepSeek R1 is doing it again on a much larger scale. RL IS THE FUTURE, as I always believed. It's the ultimate chess move in the game of intelligence.\n\n#DeepSeek #AI #rl",
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      "tweet_id": "1883243781102985669",
      "handle": "erfan_mhi",
      "text": "I don't understand why people say RLHF is a contextual bandit problem. Of course, the exploration is limited and the RL problem is badly formulated. But still, you have to solve the temporal credit assignment problem (updating all tokens) which is not part of c-bandit.",
      "created_at": "2025-01-25T17:01:21-03:00",
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      "handle": "erfan_mhi",
      "text": "RT @JeffDean: @demishassabis, James Manyika, and I wrote up a (lengthy and illustrated!) overview of the AI research work and advances acro\u2026",
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      "handle": "erfan_mhi",
      "text": "Another RL use-case in scale!",
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