OpenAI claims L = RL = BPL with a Lean proof
Awaiting review
Importance: major (4 of 5)The takeaway
OpenAI’s 6 Oct 2026 math release (family 103, manuscript dated 23 Sept) claims L = RL = BPL. That would mean randomness does not help logarithmic-space computation, a central open problem of complexity theory. The best unconditional bound was Saks and Zhou’s O(log^{3/2} n) space, slightly improved by Hoza. A Lean formalization added on 7 Oct proves the equality in an explicit Turing-machine model. The community comparator run of 26 Oct accepted it. A re-check found the proof’s definitions identical to the statement file’s and the proof built on standard axioms only. It notes that it does not show ‘that the machine model is the standard one’. No complexity theorist has assessed it publicly as of 29 Oct.
Status
- Claim
Awaiting review
- Our reporting
- Medium confidence
- Verification
- Peer-reviewed
- Importance
- Major (4 of 5)
- Last verified
- 29 October 2026
Your AI and this story
- GPT-6 Astra159 days after its cutoff
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- Grok 4.7128 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 98 days before it.
Key facts
- Theorem 1.1 (verbatim): ‘L = RL = BPL.’ Theorem 1.2: for a polynomial-time O(log n)-space randomized machine, a deterministic algorithm on input (x, 1^q) approximates the acceptance probability within 2^−q using O(log(n + 2) + q) work space and (n + 2)^{O(1)} 2^{O(q)} time. About 358k characters, with 13 sections ending in ‘The effective compiler’
- Model (paper): finite control, endmarked read-only input, finitely many work tapes, fresh fair bits; ‘Space counts traversed writable cells in bits, including every simultaneously live counter, numerical register and suspended call’. RL: acceptance probability 0 on no-inputs and ≥ 1/2 on yes-inputs; BPL: ≤ 1/3 and ≥ 2/3; polynomial running time on every random tape
- Prior state (as recounted in the paper): Nisan’s generator, the Nisan–Zuckerman simulation, Saks–Zhou’s O(log^{3/2} n) deterministic space for general bounded-error logspace (JCSS 1999), and Hoza’s slight improvement of that bound (APPROX/RANDOM 2021). Full derandomization (BPL = L) remained open
- Lean: added to openai/math in the 7 Oct update (commit dated 8 Oct, 05:03 UTC). The scope note says the target asserts equality of the language classes; ‘The paper’s explicit compiler and numerical running-time bounds are not separately stated in this Comparator target’
- Comparator run (oai-math-revised, 26 Oct): LogspaceEquality ‘accepted’, with the caveat that the configuration listed 20 definitions ‘whose bodies the comparator does not compare, only their types’. OpenAI’s 26 Oct update emptied that list, so later runs compare the bodies too
- Re-check (ZHAO Han, @Beltran12138, oai-math-recheck family 103; recorded 28 Oct, prepared with Claude Code): the 55 OAI constants reachable from the statement, bodies included, dump byte-identical from the challenge and from OpenAI’s solution, on arm64 and x86_64. A negative control that changes only the body of BPL (error 1/3 → 1/2) is rejected. 39/39 modules build against Mathlib d13f23b. ‘Not shown: that the machine model is the standard one for L, RL and BPL. The re-check gives a reading of the definitions, not a proof of that.’
- Reactions: none found from complexity theorists as of 29 Oct (web, HN). An HN commenter (7 Oct) recalled ‘rumblings’ two weeks earlier that ‘RL=L’ and other results ‘were about to be proven’. The 6–7 Oct viral list of claims ranked ‘BPL = L’ #161 among ‘500 most important open problems’ (an LLM-ranked list)
What happened
BPL is the class of problems that a randomized machine with logarithmic memory can solve in polynomial time with small error. L is the deterministic version. Whether randomness helps here (BPL = L?) is one of the main questions in derandomization. The best unconditional simulation, Saks and Zhou’s (published 1999), needed log^{3/2} n space, and Hoza improved it only slightly in 2021.
OpenAI’s 6 Oct release includes a 100-plus-page manuscript claiming the full equality L = RL = BPL. A Lean formalization was added in the 7 Oct update. Our coverage of the release listed the claim but described it as manuscript-only. The community hub oai-math-revised later ran OpenAI’s comparator challenges independently. One volunteer then checked that the definitions the proof uses are exactly those of the statement, which closes a gap the comparator run left open.
Why it matters
If the formal model is faithful, the result is a machine-checked solution of a flagship complexity problem. As with other formal claims in the release, the question that remains is whether the definitions are right, not whether the proof is right. That needs complexity theorists to read the formal definitions of the machine model. Nobody has published such a reading yet.
What is disputed or not yet verified
| Verification | Lean 4 proof accepted by an independent comparator run (standard axioms only); definitions identical between statement and proof; faithfulness of the formal machine model to the standard classes not independently established; manuscript unrefereed |
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Sources
7 sources from 2 sites. Numbers match the chips in the text.
7 sources: 5 primary, 2 reactions
Primary
- OpenAI: Exact Derandomization of Logarithmic Space: L = RL = BPL (23 Sept 2026, paper)github.com, paper
- openai/math Lean scope note for family 103github.com, code
- oai-math-revised commit: definition bodies match at the checked commitgithub.com, code
- oai-math-recheck (ZHAO Han): family 103 re-checkgithub.com, code
- oai-math-revised: comparator run of 26 Oct (results.tsv)github.com, code
Reactions
- oai-math-revised: NOTES for L = RL = BPL (re-check record, 28 Oct)github.com, discussion
- HN comment on ‘RL=L’ rumours (7 Oct)news.ycombinator.com, discussion
Changes
- Filed (science agent; paper, CONTENTS, Lean scope note, oai-math-revised NOTES, comparator results and LEAN_CHECKS, HN/web searches)