"Finetuning with Sampling: SFT Learns Better Than You Think" SFT may be better than we think, and the problem could have just been the data distribution. This paper uses MCMC to rewrite expert traces into equally correct answers that look more like what the base model would naturally produce. With that, plain SFT can outperform RL while forgetting less!

hey fam, i’m currently working on finetuning the dex, which should go live tonight or tomorrow morning. the biggest liquidity layer for robinhood will go live. thanks @blockaid_ for quickly resolving our wallet issues, as a lot of users were scared due to the warnings. the dex will start with 100k $prism/day emissions for 24 hours for testing, and then it will be increased to a much larger amount. it will mostly be dynamic and will change according to price, pool weightage, and many other factors. we are adding a few new pools too with v1, and you can farm lp to earn emissions and rewards. i’m working on an article to explain how this exactly works, and it’ll drop soon. we are also working on launching both rwa perps and the social trading layer together right after the launch of v4. here’s a personal note i wanted to add: robinhood is going through a tough battle, and it’s necessary for all the successful chains, and it will sustain this dump. pls keep on supporting projects and builders who want to build a long term defi enterprise and build the infrastructure. the dump will be temporary, but tech will be permanent. tech is what brings more retail and institutions to the chain, and that’s how ecosystems win over time. thank you for all the support all these days for $prism, but i request you guys to stay with us during this crazy robinhood dump, as i strongly believe it’s gonna last for a week and it will be back stronger. whatever, we keep shipping let’s build fam.

One open question for anyone finetuning models is - how "post-trainable" are different open source models? We can theorize that models that reside in shallower loss curves are more amenable to post-training - which makes sense, given that it requires less gradient updates to modify behavior. Then, this paper proposes a pre-training algorithm that makes a model more post-trainable. The idea is straightforward once you wrap you head around it: 1. Find the worst policy locally around the policy pi (found by taking the inverse gradient w.r.t pi), call that pi_prime 2. Take the gradient at pi_prime (call that grad[pi_prime] ) 3. Apply grad[pi_prime] to pi. The intuition is that we're actually moving in the direction that benefits the worst model around you, meaning a model maintains post-trainability because we maintain a shallow loss landscape. Another way to imagine this, is it effectively avoids steep pot-holes during pretraining that would lock your model in a distribution that it can't post-train its way out of. Really great work from @IshaanWatts18, @CatherineL11638, @goyalsachin007, @jacspringer, @AdtRaghunathan. It's our favorite paper of the month at Trajectory!

Debate Training Reduces Reward Hacking in RLAIF New paper from Google DeepMind: "We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward hacking compared to a reinforcement learning from AI feedback (RLAIF) baseline." paper link:

hey fam, (consider this tweet an ama and ask me anything about prism protocol, prism L3 or anything around the product) im currently working on finetuning V5 for launch but here’s a few things i wanted to share: the whole purpose of v5 is to make the site very smooth and less confusing for users. but that’s not it, we are also working on adding better verification of each asset, multiple liquidity pools aggregated for similar assets under one router, every feature and tool will be upgraded with better efficiency and also better rewards. this version along with the trading competition will act as our first onboarding event and allow us to move on auto pilot for a while. when i say verification, it’s not a simple verification. It will be multiple layers of asset verification, proof of reserves, better osint and self verification by issuers too. enterprise grade verification for each asset in our protocol. also perps will go live, futures and options will go live, dex v2 will go live, higher emissions and rebates will go live, and efficient trading flow too. not everything at once of course, one by one. and a better developer environment will be built for prism test net and more dapps will go live. but when i keep saying more, we are not focusing on just increasing numbers here lol anything that’s not up to industry standard or not efficient will be instantly delisted from the protocol. more dex pools, more transparency layers, better docs and guides etc. this version should mark the beginning of prism as a mainstream platform for RWAs just like how every other protocol takes over and dominates one particular space. whatever, we keep shipping. let’s build.

hey fam, good morning our first dapp went live yesterday on prism testnet and we already have more than 100 users spending gas on the testnet. also seems like a lot of people now have a better understanding of how prism L3 works compared to last time haha. today we will work on: > finetuning V5 for launch and restructuring the protocol features to make it easy, smooth, less confusing and not exhausting > add and polish all the dex pools with the farm V2 contract for improved emissions and rewards and to build a much better liquidity layer. > work on a few important partnerships add 1000 more cool assets covering $10B in market value > improve OSINT, proof of reserves and verification. > work on finetuning RWA perps for launch > work on installable web apps a lot of people reached out to me and asked in the community how verification is done. you can just go to any asset page or OSINT tool to find out everything about an asset, from its verification to exact custodial information. we are also working on building a network where we can get consistent weekly proof of reserves and expose any asset that fails verification. we also need to fix a few of the bugs and UI issues within the protocol (specifically mobile UI). whatever, we keep shipping. lets build for 16 hours straight today, together. btw the total $PRISM supply staked also went up to $1.2M😃 letsgoooooooo

A robot that runs a video generation model as its policy! 🧤 I ran into mimic at NVIDIA GTC in Berlin. @mimicrobotics's five-finger hands built in house, two wrist cameras plus a workspace camera, their own whole-body stack doing impedance control on the arms. The policy is FLUX-mimic, developed with Black Forest Labs on the FLUX 3 backbone. The action decoder is lightweight and reads intermediate features from the backbone’s video prediction path. Over 95% of training compute went into predicting video. Actions are a thin readout on top. And it works with the backbone frozen. Black Forest Labs reports the decoder beating prior VLAs with no backbone finetuning at all, a setting where VLAs reportedly fail outright. The manipulation-relevant structure is already sitting in the video representation. Nobody taught it contact or cause and effect as a separate objective. It fell out of predicting tens of millions of hours of video. One detail from the demo is the drop-in teleop. The operator takes over mid-episode to correct a wrong move, then hands back and the policy resumes. That’s the safety story, and every correction is a labelled intervention at precisely the moment the policy was wrong. Running at Audi on kitting, ECU insertion, and soft parts like seals and cables. Thanks for explaining @elvisnavah! 🦾 It was awesome during @NVIDIARobotics GTC, thanks for inviting me! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

hey fam, good morning. weekends back. lets spend this weekend resolving issues within the protocol, fixing bugs etc. today, we will work on: > finetune and polish V5 for launch > work on installable web apps > fix the mobile UI (its been a while since we last upgraded our mobile UI haha) > work on RWA perps and F&O > launch and test more dapps on prism L3 > add more assets and aggregate more liquidity. > permissioned dexs and V4 hooks > finetuning the asset marketplace > issuer vaults and more synthetics and many more, i just listed a few. everyones slowly talking about mass tokenization and with SEC exempt about to go live next month, we will see thousands of issuers issuing thousands of tokens. but no ones really thinking about the accessibility and scalability of those tokens and they will all come to prism. lets discuss more and build together for 16 hours straight today as we did last weekends. letsgoooooo

Quick lookback on technical AI themes of 2026: (dataset: daily ainews title stories) Jan: - Ralph Loops - RLMs - Filesystems are all you need - X LLM recsys open sourced - X Grok Imagine #1# Video model - Anthropic blocked OpenCode / OpenClaw - MCP Apps - Moltbook - Apple picks Gemini for new Siri - Notable models: Kimi K2.5 - Fast inference: OAI x Cerebras partnership - Big raises: xAI @ $230B + SpaceX, OpenEvidence @ $12B Feb: - Codex App launch - Claude Code 1 year anniversary - Context Graphs - Fast Inference: Taalas 17k tok/s/user Llama 3 8B - Anthropic vs Deepseek/Moonshot/Minimax distillation accusation - Notable models: Opus/Sonnet 4.6, 5.3 Codex and Codex Spark, Qwen-Image 2.0, Seedance 2.0, GLM-5, Gemini 3.1 Pro and Deep Think, Nano Banana 2 - Big raises: OpenAI @ $840B, Anthropic @ $380B, ElevenLabs @ $11B, Cerebras @ $23B Mar: - T3 Code - Autoresearch - Claude Code source leak - Claude Cowork Dispatch - OpenAI buys Astral - Cloudflare-Next.js "Slop Fork" - Meta buys Dreamer - NVIDIA GTC: OpenClaw, Vera CPU, 1T sales backlog, "The Inference Inflection" - ARC-AGI 3 introduced, SOTA models are 1% - Notable models: GPT 5.4 unifies chat and codex, Gemini 3.1 Flash-Lite/Live, Nemotron 3 Super, Composer 2, MiniMax 2.7, Arcee’s Trinity-Large-Thinking - Big Raises: Yann LeCun AMI Labs $1B seed Apr: - Claude Design - OpenAI Symphony & Harness Engineering discourse - @LilianWeng writeup later - OpenAI and Microsoft break up - Tokenmaxxing fears - Codex Superapp - AIE Europe, AIEi Miami - Hermes Agent gains traction w/ Manim skill - Cyber: Anthropic Project Glasswing - Notable models: Claude Mythos "Capybara", Opus 4.7, Muse Spark, Gemma 4, Kimi K2.6, GPT-Image-2, GPT 5.5 "Spud", DeepSeek v4 Pro/Flash Base/Instruct on Huawei Ascend, Poolside Laguna XS.2, Grok 4.3 - Big Raises: not much but Anthropic $19B->$30B ARR in one month... May: - Cyber: OpenAI Daybreak, Tanstack Mini-Shai Hulud - FDE: OpenAI DeployCo, Ode by Anthropic - OpenAI solve Erdos planar unit distance problem - OpenAI deprecates finetuning - "How to land a job at a frontier lab" - Anthropic rents Colossus I from SpaceX for $5B/yr - Thinking Machines Interaction Models (still underrated!!!) - Notable models: GPT-Realtime-2/Translate/Whisper, Gemini 3.5 Flash, Omni, Antigravity 2.0, Opus 4.8, Cosmos 3, Minimax M3 - Big Raises: Fireworks @ $15B, Baseten @ $11B, Cognition @ $26B, Cerebras $60B IPO, Anthropic @ $965B Jun: - Claude Tag - OpenAI Jalapeno - "The Untrainable" - @saranormous - Loopcraft: The Art of Stacking Loops - Metaharnesses discourse? Flue v Eve - Zai Prof @jietang predicts Open Fable by Dec - "SpaceX is a $28B/yr neocloud" - @jaminball - Anthropic-Fable White House issue (resolved) - Notable models: GPT 5.6 Sol/Terra/Luna, Fable 5, Sonnet 5, MAI-Thinking-1 and MAI Family, Nemotron 3 Ultra, GLM 5.2, Reve 2/ Ideogram 4 - Big Raises: SpaceXai buys Cursor for $60B, Supabase @ $10B Jul: - AIE World's Fair 3 - OpenCode 2.0 - Cyber: OpenAI-HuggingFace incident - Chinese Model ban discourse - GPT 5.6 Luna 80% price cut - Notable models: Grok 4.5, Opus 5, Muse Image, Thinky Inkling, Kimi K3, BFL FLUX 3, DeepSeek-v4-Flash-0731 - Big Raises: Aug: - EU AI text watermarking - Microduck - Megakernels - Memory prices up 500% in 12 months - How to steal a Reasoning Trace - Grok @bot! - OpenAI shuts off Cursor - Fast Inference: AMD buys Taalas - @andrewng stars AI Engineering series - Hot Chips: Jalapeno, Cerebrase CS-5, Groq 3, Apple M6 - Demis steps down from CEO, & Dean et al leave - Notable models: Qwen 3.8 Max, Muse Spark 1.2, Muse Glimmer, Grok 4.6, Gemini 3.7 Flash, GLM 5.3, @Fal H3 Max Live - Big Raises: Stripe buys OpenRouter $7B, Poolside $12B reverse-execuhire to NVIDIA, NVIDIA buys HuggingFace for $13B Sep: - AIEi Paris - OpenAI Navier-Stokes singularity - collusion . wiki - Yegge shuts down Gas Town - AEF-1 standard for Third Party Evaluators - Meta Muse App launch. Also new glasses and Charm - OpenAI blocks Cursor - OpenAI Dots - Opus 5.5 explainer videos - Pi 1.0 & Pi Durable - Notable Models: Fable 5.1, Mythos 5.1, Opus 5.5, GPT-6 Astra, GPT Image 2.5, DeepSeek v4.1-Flash, @typesafeai Jev, MiMo-v2.6-Pro new Open SOTA, GPT 6.1 Sol & Ultrafast & Decisions API, Gemini 4 Argon - Big Raises: Cognition @ $48B, Mistral @ $24B, AMD buys World Labs for $8.2B Oct to date: - AIE NYC - @dhh stops using Rails - ArtCraft clones Adobe suite - Interpretability vs Polysemanticity - OpenAI Quasi-Riemann-Hypothesis & 722 Math - Notable Models: Reflection Beam, Claude Haiku 5.5, Muse Wonder & Wonder Full reach Opus 5.5 tier - Big Raises: TypeSafe $7.5B 3 weeks after launch, Modal @ $16B (this was done for research purposes for the keynote in Nov, just sharing notes in case helpfuil)

All that agent trace data sitting in your production logs — what if it could train its own specialized model? LangSmith just made that a built-in workflow. Title: Introducing LangSmith Fine-Tuning URL: ❓ What is LangSmith Fine-Tuning? It's the `smithtune` CLI, which turns production agent traces into a fine-tuned model without building custom infrastructure. It covers dataset creation, training, evaluation, and deployment end to end. ❓ How does it build the training data? It pulls trajectories — ordered sequences of messages and tool calls — from LangSmith projects, then uses a multi-agent review process with custom rubrics to keep only high-quality examples. Crucially, it preserves the exact context at each turn, including which tools were available. ❓ How do training and deployment work? Supervised fine-tuning via LoRA runs through managed platforms like Fireworks and Baseten, so there's no GPU provisioning to manage. After a replay evaluation against the base model, `smithtune deploy` ships the fine-tuned model straight to production. ❓ What results did they see? On an issue-detection task, fine-tuning lifted Kimi K3's score from 90.0 to 96.0. On code review, it matched or beat base-model quality while cutting model calls by 29.8% and tool requests by 29.4%. #LangSmith# #FineTuning#