Conjecture: Intellect‑2 remains largely unchanged after decentralized training

Prove or refute that the Intellect‑2‑32B language model trained via decentralized reinforcement learning from an instruction‑tuned checkpoint remains largely unchanged relative to its original model, as evidenced by negligible gains on optimized math benchmarks; specifically, ascertain whether the training procedure meaningfully alters the model’s parameters or capabilities beyond the baseline.

Background

In the high‑data reasoning‑training scenario, the paper evaluates several instruction‑tuned models subjected to large‑scale supervised or reinforcement learning on reasoning traces. Among these, Intellect‑2‑32B exhibits minimal forgetting and minimal backward transfer, with negligible improvements on math benchmarks optimized for such training.

The authors conjecture that the decentralized training procedure may leave the model largely unchanged compared to its original checkpoint, implying that the observed minimal changes could stem from limited effective training impact rather than true capability gains. Establishing whether this conjecture holds would clarify the dynamics of forgetting and backward transfer in decentralized training setups and inform the interpretation of benchmark outcomes.