CEO @bespokelabsai. RL/Envs/Posttraining/Agents. Created Generative Retrieval at @GoogleDeepMind.

Inside an RL Env
Joined February 2008
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Replying to @Prakhard3
Yeah not needed as a standalone thing. Claude code has a nice terminal as well
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Mahesh Sathiamoorthy retweeted
Seeing generational levels of locked-in on the streets of SF (face redacted to protect the innocent 🫡)
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The first thing I usually do when I get a new computer is to install iTerm. I am thinking in the next one I may entirely try to avoid installing iTerm. For that matter, I suppose I can uninstall iTerm and see what happens..
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Replying to @stephzhan
Congrats @stephzhan! Super exciting!
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Remember Oct 21 2026 as the day a very long security winter began.
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Why is this PR for Anthropic?
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Good to see Bespoke Nimble at #3 on the API providers page of Jevbench, above Jev 1.13.0 and Jev 1.14.0. benchmarkheaven.com/jev-mode… It's available here: console.bespokelabs.ai/
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Mahesh Sathiamoorthy retweeted
"Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs!" Bespoke has found several issues using our internal QC agent, and has been working on improving Terminal Bench over the months. Here are the 25 issues we filed, and there are PRs corresponding to them. A subset of these were what @EpochAIResearch also used in their report: epoch.ai/benchmarks/terminal… All of this shows that building RL envs is hard, and building a benchmark is harder github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… and more below
The @SnorkelAI team did an audit of these findings using our internal QC platform. Tl/dr: We agree with many of the findings, but ~55% of the fixes from @preferencemodel had their own issues with reward hacking or over-prescriptiveness. Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs! Building datasets like this - and maintaining ambitious open benchmarks around them - is hard. We believe it requires open, collaborative review with community maintainers and relevant domain experts to get right. @terminalbench was one of the first community efforts to build an open, continuous benchmark, and drive a vision of how OSS contributes to the rapidly advancing frontier of AI. We hope that the community will invest in working collaboratively with benchmark maintainers and task authors to keep this vision alive. --- Some takeaways + details of our preliminary audit: (1) We confirm 100% of the reward hacking / verification gap findings using our QC system. However, we find that 17/44 (39%) of the corrected tasks are just as or more reward hackable. Additionally, we don’t yet see evidence that these verification gaps meaningfully affected benchmark rollouts within CIs, though our analysis here is still ongoing. (2) Our preliminary review raises questions about the underspecification findings for 22/41 (54%) of the flagged tasks, including 7 explicitly marked by task authors as intentional design choices. These cases warrant expert peer review and adjudication: balancing over/under-specification is a critical and tricky design decision - much of human intelligence concerns dealing correctly with reasonable and realistic underspecification - and we believe these corrections made to the tasks are likely too over-specified, reducing realism and difficulty relative to spec. (3) Around 40% of the issues found were already tracked in the upstream repo (including 21 tasks with outstanding fix PRs)! OSS projects require community efforts to correct identified open issues, and ideally audits like these are done collaboratively with task authors and maintainers directly, in the traditional community spirit that has kept OSS alive for decades.
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Love this!
I launched the first image and video generation models at Google, and kept asking why all AI designs look the same. AI has brought us the WORST era of design. It made content creation effortless, but it also made 90% of it look painfully generic. My co-founder and I spent 2 years building in stealth to change that. Meet Vyb, the first AI design model that understands your taste like a designer would. RT + comment your website URL and we’ll make your first design.
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Replying to @j_mcgraph
"Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs!" Bespoke has found several issues using our internal QC agent, and has been working on improving Terminal Bench over the months. Here are the 25 issues we filed, and there are PRs corresponding to them. A subset of these were what @EpochAIResearch also used in their report: epoch.ai/benchmarks/terminal… All of this shows that building RL envs is hard, and building a benchmark is harder github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… and more below
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Replying to @ellen_in_sf
"Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs!" Bespoke has found several issues using our internal QC agent, and has been working on improving Terminal Bench over the months. Here are the 25 issues we filed, and there are PRs corresponding to them. A subset of these were what @EpochAIResearch also used in their report: epoch.ai/benchmarks/terminal… All of this shows that building RL envs is hard, and building a benchmark is harder github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… and more below
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"Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs!" Bespoke has found several issues using our internal QC agent, and has been working on improving Terminal Bench over the months. Here are the 25 issues we filed, and there are PRs corresponding to them. A subset of these were what @EpochAIResearch also used in their report: epoch.ai/benchmarks/terminal… All of this shows that building RL envs is hard, and building a benchmark is harder github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… and more below
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"Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs!" Bespoke has found several issues using our internal QC agent, and has been working on improving Terminal Bench over the months. Here are the 25 issues we filed, and there are PRs corresponding to them. A subset of these were what @EpochAIResearch also used in their report: epoch.ai/benchmarks/terminal… All of this shows that building RL envs is hard, and building a benchmark is harder github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… github.com/harbor-framework/… -> github.com/harbor-framework/… and more below
The @SnorkelAI team did an audit of these findings using our internal QC platform. Tl/dr: We agree with many of the findings, but ~55% of the fixes from @preferencemodel had their own issues with reward hacking or over-prescriptiveness. Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs! Building datasets like this - and maintaining ambitious open benchmarks around them - is hard. We believe it requires open, collaborative review with community maintainers and relevant domain experts to get right. @terminalbench was one of the first community efforts to build an open, continuous benchmark, and drive a vision of how OSS contributes to the rapidly advancing frontier of AI. We hope that the community will invest in working collaboratively with benchmark maintainers and task authors to keep this vision alive. --- Some takeaways + details of our preliminary audit: (1) We confirm 100% of the reward hacking / verification gap findings using our QC system. However, we find that 17/44 (39%) of the corrected tasks are just as or more reward hackable. Additionally, we don’t yet see evidence that these verification gaps meaningfully affected benchmark rollouts within CIs, though our analysis here is still ongoing. (2) Our preliminary review raises questions about the underspecification findings for 22/41 (54%) of the flagged tasks, including 7 explicitly marked by task authors as intentional design choices. These cases warrant expert peer review and adjudication: balancing over/under-specification is a critical and tricky design decision - much of human intelligence concerns dealing correctly with reasonable and realistic underspecification - and we believe these corrections made to the tasks are likely too over-specified, reducing realism and difficulty relative to spec. (3) Around 40% of the issues found were already tracked in the upstream repo (including 21 tasks with outstanding fix PRs)! OSS projects require community efforts to correct identified open issues, and ideally audits like these are done collaboratively with task authors and maintainers directly, in the traditional community spirit that has kept OSS alive for decades.
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We have some preliminary numbers ready. I can't seem to DM you though because we wanted to check a few things.
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Gemini has improved quite a bit for autoresearch/ RSI with Argon. Congrats!
We added Gemini 4 Argon to our AutoResearchExam Pareto curve. It is 2-3 times cheaper compared to the other frontier models for almost the same ML engineering performance. Congratulations to the GDM team.
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