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RA3: Mid-Training with Temporal Action Abstractions for Faster Reinforcement Learning (RL) Post-Training in Code LLMs

ByRicardo

TL;DR: A brand new analysis from Apple, formalizes what “mid-training” ought to do earlier than reinforcement studying RL post-training and introduces RA3 (Reasoning as Action Abstractions)—an EM-style process that learns temporally constant latent actions from knowledgeable traces, then fine-tunes on these bootstrapped traces. It exhibits mid-training ought to (1) prune to a compact near-optimal motion subspace and (2) shorten the efficient planning horizon, enhancing RL convergence. Empirically, RA3 improves HumanEval/MBPP by ~8/4 factors over base/NTP and accelerates RLVR on HumanEval+, MBPP+, ResideCodeBench, and Codeforces.

What does the analysis current?

The analysis crew current the primary formal remedy of how mid-training shapes post-training reinforcement studying RL: they breakdown outcomes into (i) pruning effectivity—how properly mid-training selects a compact near-optimal motion subset that shapes the preliminary coverage prior—and (ii) RL convergence—how shortly post-training improves inside that restricted set. The evaluation argues mid-training is best when the resolution house is compact and the efficient horizon is brief, favoring temporal abstractions over primitive next-token actions.

https://arxiv.org/pdf/2509.25810

Algorithm: RA3 in one cross

RA3 derives a sequential variational decrease sure (a temporal ELBO) and optimizes it with an EM-like loop:

  • E-step (latent discovery): use RL to deduce temporally constant latent buildings (abstractions) aligned to knowledgeable sequences.
  • M-step (mannequin replace): carry out next-token prediction on the bootstrapped, latent-annotated traces to make these abstractions a part of the mannequin’s coverage.

Results: code technology and RLVR

On Python code duties, the analysis crew stories that throughout a number of base fashions, RA3 improves common cross@ok on HumanEval and MBPP by ~8 and ~4 factors over the bottom mannequin and an NTP mid-training baseline. In post-training, RLVR converges sooner and to larger ultimate efficiency on HumanEval+, MBPP+, ResideCodeBench, and Codeforces when initialized from RA3. These are mid- and post-training results respectively; the analysis scope is code technology.

Key Takeaways

  1. The analysis crew formalizes mid-training through two determinants—pruning effectivity and affect on RL convergence—arguing effectiveness rises when the choice house is compact and the efficient horizon is brief.
  2. RA3 optimizes a sequential variational decrease sure by iteratively discovering temporally constant latent buildings with RL after which fine-tuning on bootstrapped traces (EM-style).
  3. On code technology, RA3 stories ~+8 (HumanEval) and ~+4 (MBPP) common cross@ok features over base/NTP mid-training baselines throughout a number of mannequin scales.
  4. Initializing post-training with RA3 accelerates RLVR convergence and improves asymptotic efficiency on HumanEval+, MBPP+, ResideCodeBench, and Codeforces.

Editorial Comments

RA3’s contribution is concrete and slender: it formalizes mid-training round two determinants—pruning effectivity and RL convergence—and operationalizes them through a temporal ELBO optimized in an EM loop to be taught persistent motion abstractions earlier than RLVR. The researchers report ~+8 (HumanEval) and ~+4 (MBPP) common cross@ok features over base/NTP and sooner RLVR convergence on HumanEval+, MBPP+, ResideCodeBench, and Codeforces.

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