@anshuman1

LLM Post-Training/ Recursive Self-Improvement for Reasoning Co-founder/ MTS @ReasonCoreAI PhD @UCLA, Bachelors @iitbombay Led a team of ~500 PhD researchers

Palo Alto, CA
Joined June 2009
Anshuman Lall @ NeurIPS 2026 retweeted
My coding agents are running. Figured I should too
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Anshuman Lall @ NeurIPS 2026 retweeted
A decision-model leaderboard can rank the serving setup as much as the model. JevBench separates open-weight systems from hosted APIs: the former run on the same GPU, while API latency includes the network and providers' delivery choices. Its self-hosted latency adjustment is an estimate, not a measurement of a deployed service. Compare capability on common tasks, then measure speed and cost in the environment where you will actually use it. benchmarkheaven.com/jev-mode…
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Anshuman Lall @ NeurIPS 2026 retweeted
Meta's Muse Wonder launch puts memory and persistence at the center of the model story. Meta calls them Active Memory and Active Focus: bringing back relevant context, trying another approach when work stalls, and checking before important actions. That's a useful direction for agents. The test now is whether those behaviors hold across unfamiliar tasks, changing requirements, and long projects, not just launch demos. Wonder is available through Meta's API and Muse Code; Wonder Full is available to Muse Code subscribers. research.meta.ai/blog/muse-w…
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Anshuman Lall @ NeurIPS 2026 retweeted
A higher public score is not the same as a better research result. DeepRSI, announced by @jparkjmc, gives agents 48-hour budgets to improve ML baselines, with a public verifier for feedback and a private verifier for scoring. The useful test is whether improvements survive held-out evaluation after a long optimization loop. That's evidence about research-task performance, not proof of recursive self-improvement. Longer runs need stronger checks, not just more time. deeprsi.com/blog/
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Anshuman Lall @ NeurIPS 2026 retweeted
Model Benchmarks Are Worthless How can a model score 24% and 59% on the same 113 coding tasks? It happens when you vary the harness that wraps it. I’ve increasingly ignored most of the recent model leaderboard results. Partly from exhaustion and partly because improvements in a ranking don’t show up in the actual work. So I asked the 8090 Labs team to measure what the leaderboards skip: the model and the harness together, on real bug fixes and features from open-source projects. 8090 Labs ran 7 models through 5 harnesses. Here are a few results I didn't expect: - Anthropic's Claude Fable 5.1 scored 41% in Claude Code and 61% in OpenAI's Codex. It made 28 tool calls at the median in one and 66 in the other. - The cheapest runs weren't the cheapest work. GLM in Codex cost $0.22 a run and $0.93 per success. In Pi it cost $0.25 a run and $0.43 per success. - GLM passed a task on both attempts 51 times in one harness and 16 times in another. - GPT-5.6 Sol and Grok 4.6 barely cared which harness they were in. The bottom line is that if you're picking a model/harness combo off a model-only leaderboard, you're making a critical decision based on only half the information. 8090.ai/blog/same-model-diff…
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Anshuman Lall @ NeurIPS 2026 retweeted
Le Chonk cooked on visual grounding. Pinpoints exactly where things are in an image. 42% on Dense 200, ahead of even frontier closed models..
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Anshuman Lall @ NeurIPS 2026 retweeted
MUSE WONDER 🍉 muse wonder is the best model in the world for personal agents and professional agents. we evaluated our model against the best models in the world across agentic and coding tasks, and we think you all will like it.
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Anshuman Lall @ NeurIPS 2026 retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
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Note: ~700K tokens in one rollout!!
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as demand shifts—live executions never interrupted. Up to 1.85× speedup over Colocate, 1.78× over Async. - Trajectory Processor: turns branching executions (compaction, sub-agents, retries) into prefix-shared trajectory trees with bounded, deduplicated training samples. Partial progress after timeout still gets scored. Scaled to Qwen3.8-2.4T-A95B with ~700K tokens per rollout, QwenGyre lifts NL2RepoBench from 52.5% to 58.5% in 48 steps, cutting end-to-end training time by ~44% vs Async. 🔗 Read the paper: arxiv.org/abs/2609.33848
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Anshuman Lall @ NeurIPS 2026 retweeted
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as demand shifts—live executions never interrupted. Up to 1.85× speedup over Colocate, 1.78× over Async. - Trajectory Processor: turns branching executions (compaction, sub-agents, retries) into prefix-shared trajectory trees with bounded, deduplicated training samples. Partial progress after timeout still gets scored. Scaled to Qwen3.8-2.4T-A95B with ~700K tokens per rollout, QwenGyre lifts NL2RepoBench from 52.5% to 58.5% in 48 steps, cutting end-to-end training time by ~44% vs Async. 🔗 Read the paper: arxiv.org/abs/2609.33848
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Anshuman Lall @ NeurIPS 2026 retweeted
yes i was @ COLM for their efficient reasoning workshop, yes i believe metareasoning ought to be a first class training objective, yes i think chinese models now have a ton more RLHF, yes we shouldnt waste SO MANY TOKENS. online RL envs are the future
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Anshuman Lall @ NeurIPS 2026 retweeted
The COLM Keynote talk on Research at AI Speed has slides here: hunch.net/~jl/Research_at_AI… . Here we try to answer the conversation I keep having with folks around "How do I do something useful?"
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Anshuman Lall @ NeurIPS 2026 retweeted
AIPOCH Open-Science v0.37.1 v0.37.1 is a maintenance release focused on accuracy, stability, and smaller installation packages. This update improves scientific PDF structure extraction, Windows path compatibility, PDF translation, and Notebook execution, while reducing Desktop Installer and Standalone Runtime sizes. 【🚀 Key Updates】 📄 Improved PDF Structure Extraction Scientific PDF extraction now preserves document structure more accurately. Figures retain complete Captions and Legends without being incorrectly split by panel references. Ruled Tables near formulas are reconstructed correctly. Captions, Graphics, and Cell Text are assigned to their proper elements. Table Records, Grouped Headers, and Row Groups are restored correctly. Extracted Table Cells preserve Source References and Rich Text for reliable evidence tracking. 🪟 Windows Non-ASCII Path Support Windows Desktop CLI now supports installation paths containing Chinese and other non-ASCII characters. The CLI Launcher no longer depends on legacy code-page behavior and preserves the caller's original Code Page and Exit Code. 🌐 Windows PDF Translation Fixes Local PDF translation is now more reliable across Windows drive paths. Fixed Unsupported Module URL errors when WASM assets are located on Windows drives. Improved translated PDF generation when embedded fonts use different names for the same Glyph. Preserved validated Glyph rendering for better layout fidelity. 📓 Quieter Notebook Execution Notebook background operations no longer open unexpected Console Windows or Windows Terminal instances. This applies to Kernel execution, Environment Fingerprinting, Package Inventory, and Lock Capture, providing a smoother desktop experience during long-running tasks. 📦 Smaller Installation Packages Unused Runtime payloads have been removed, including: - Duplicate bundled dependencies - Unused Model Runtimes and Tokenizer Assets - Platform libraries not loaded by the application Both Desktop Installer and Standalone Runtime downloads are now smaller, addressing package-size growth introduced by the v0.37.0 Backend Split. 【🔧 Improvements】 Improved scientific PDF layout and content attribution. More reliable Table reconstruction and evidence provenance. Better translated PDF rendering fidelity. Reduced installation and Runtime download sizes. Smoother background Notebook operations on Windows. 【🐛 Bug Fixes】 PDF & Literature — Fixed fragmented Figure Captions, incorrect Panel Crops, formula-adjacent Table detection, and misplaced PDF elements. Table Extraction — Restored Grouped Headers, Row Groups, Source References, and Rich Text. Windows Runtime — Fixed CLI execution in non-ASCII installation paths and preserved Code Page and Exit Code behavior. PDF Translation — Fixed Windows WASM asset loading and embedded-font Glyph validation. Notebook — Removed unexpected Console and Terminal windows during background execution and environment checks.
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Anshuman Lall @ NeurIPS 2026 retweeted
everyone's waiting for RSI, AI recursively improving itself. meanwhile the actual acceleration is pretty old school: humans using the best current model to build the next one. that's not RSI. it's a really good tool loop with people still in it.
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Anshuman Lall @ NeurIPS 2026 retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Anshuman Lall @ NeurIPS 2026 retweeted
OpenRouter’s monthly token traffic has risen from 2 trillion in January 2025 to 379 trillion as of July 2026, with open-weight models accounting for 75% of token generation. $NVDA $MSFT $META $GOOG $AMZN
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Anshuman Lall @ NeurIPS 2026 retweeted
AI agent swarming will simultaneously be a massive force for good and evil — like any powerful technology (think nuclear).
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Anshuman Lall @ NeurIPS 2026 retweeted
Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything uses world models to guide action selection and learning. Feedback improves the policy or workflow and, when needed, the world model. Validated updates carry forward to shape the next round of experience. Across code, GUI interaction, laboratory planning, and reaction optimization: • Code-repair success: 26.3% → 31.2%, with the coder fixed • GUI agent task: 39.4% fewer actions, with higher completion • Lab-planning scores: 21.80% → 46.01% over five policy versions • Mean best observed reaction yield: 96.15% vs. 82.16% for Bayesian optimization, under the same 40-experiment budget 📄 Paper: arxiv.org/abs/2610.13912 💻 Code: github.com/Gen-Verse/RSI-Any… Watch the video below 👇 #RSI #WorldModels #RSI #AIAgents #AIforScience #JEPA
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Anshuman Lall @ NeurIPS 2026 retweeted
#IITBayArea Leadership Conference this coming weekend! Hope to meet you there. Having helped steer this community behind the scenes, I can confirm the keynote is merely the warm-up act for the inevitable hallway brainstorming session.
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Anshuman Lall @ NeurIPS 2026 retweeted
Decision models like Jev are getting a lot of hype right now, but are decision models I actually probabilistically coherent? What happens if you flip the predicate from "it will rain" to "it will NOT rain"? Turns out, not only do they give incoherent answers (e.g., P(A) + P(not A) > 1), but they're sometimes WORSE than standard LLMs. I recreated this work from @SuproteemSarkar and Isaiah Andrews on a simple weather forecasting task using OpenAI's new decision model.
suppose you ask a language model the probability it will rain tomorrow. then you ask the probability it will not rain tomorrow whatever happens tomorrow, those two probabilities should sum to one but suppose the model generates P(rain) = 0.7 and P(no rain) = 0.2
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