Alex J. Champandard

Alex J. Champandard

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Building tools and teams where humans ≫ machines. AI, ML, research & development. co-Founded #CreativeAI #⚘

Meta's Soft Tokens Yield Modest Gains on GSM8k
SocialOct 23, 2025

Meta's Soft Tokens Yield Modest Gains on GSM8k

This paper from Meta about "Soft Tokens" in RL is interesting; it allows LLMs to invent their own non-discrete (recursive) representations in order to solve problems better... Results are mixed though: it's only a few percent better on GSM8k from pass@4...

By Alex J. Champandard
AI Firms Launch Browsers to Dodge Scraping Liability
SocialOct 23, 2025

AI Firms Launch Browsers to Dodge Scraping Liability

The reason AI companies are rushing to release browsers: they don't want the responsibility / liability of scraping on their servers. They need to push that to the users! We'll be moving into an ever more gated internet soon...

By Alex J. Champandard
Only Path to Shippable Code From AI Agents?
SocialOct 21, 2025

Only Path to Shippable Code From AI Agents?

Is this the only way to get coding agents to produce shippable quality code? https://t.co/bZvxMN6JEv

By Alex J. Champandard
More Compute Trumps All Other AI Strategies
SocialOct 20, 2025

More Compute Trumps All Other AI Strategies

Without checking, what is the message behind the "Bitter Lesson", in your opinion? (a) all other things being equal, using more compute is better. (b) more compute is better than all the other things put together.

By Alex J. Champandard
Internal AI May Replace Risky Open‑Source Contributions
SocialOct 20, 2025

Internal AI May Replace Risky Open‑Source Contributions

Even though this particular example worked out as you'd expect today, Open Source dynamics will certainly change. Accepting and merging contributions is always a risk and has high cost, so trusting an internal AI system for minor codebase improvements may become...

By Alex J. Champandard
Avoid the RL Hammer: Choose the Right Tool
SocialOct 20, 2025

Avoid the RL Hammer: Choose the Right Tool

If you sub-optimally define any problem to require RL, when most can be solved with different approaches, then of course the RL hammer looks like the right solution! Rather than defending RL thru semantics, better would be to ask: how can...

By Alex J. Champandard
RL Fine‑tuning LLMs Caps at 61% Success Rate
SocialOct 18, 2025

RL Fine‑tuning LLMs Caps at 61% Success Rate

It's hard to overstate how devastating this paper is, not only for reinforcement learning. They spent $4m of compute to find out that RL on LLMs basically taps out at 61% "asymptotic pass rate" (exact rate depends on context), but they...

By Alex J. Champandard